human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
  Abstract: Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-2 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning"):
  with members:
    - "feasibility of fog computing"
    - "defog fog computing benchmarks"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "enorm a framework for edge node resource management"
    - "fog radio access networks mobility management interference mitigation and resource optimization"
    - "a survey and taxonomy of resource optimisation for executing bag of task applications on public clouds"
    - "challenges and opportunities in edge computing"
    - "decentralized computation offloading game for mobile cloud computing"

Connected through hyperedge HE-3 (induced by source paper "edge computing enabled smart cities a comprehensive survey"):
  with members:
    - "a federated capability based access control mechanism for internet of things iots"
    - "cache enabled hetnets with millimeter wave small cells"
    - "energy efficient joint offloading and wireless resource allocation strategy in multi mec server systems"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "a game theoretic approach to multi objective resource sharing and allocation in mobile edge"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "a taxonomy for management and optimization of multiple resources in edge computing"
    - "toward low latency and ultra reliable virtual reality"

Connected through hyperedge HE-4 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems"):
  with members:
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "optimal resource allocation for wireless powered mobile edge computing with dynamic task arrivals"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "deep reinforcement learning for online offloading in wireless powered mobile edge computing networks"
    - "a survey on mobile edge computing the communication perspective"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "collaborative computation offloading in wireless powered mobile edge computing systems"):
  with members:
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "wireless powered user cooperative computation in mobile edge computing systems"
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "joint tx power allocation and rx power splitting for swipt system with multiple nonlinear energy harvesting circuits"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "price based distributed offloading for mobile edge computing with computation capacity constraints"
    - "a survey on mobile edge computing the communication perspective"
    - "multi antenna enabled cluster based cooperation in wireless powered communication networks"

Connected through hyperedge HE-6 (induced by source paper "optimal resource allocation for wireless powered mobile edge computing with dynamic task arrivals"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "communications and signals design for wireless power transmission"
    - "a general utility optimization framework for energy harvesting based wireless communications"

Connected through hyperedge HE-7 (induced by source paper "an online reinforcement learning approach to quality cost aware task allocation for multi attribute social sensing"):
  with members:
    - "the age of social sensing"
    - "towards reliable online clickbait video detection a content agnostic approach"
    - "zero shot deep reinforcement learning driving policy transfer for autonomous vehicles based on robust control"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "dynamic task offloading and resource allocation for ultra reliable low latency edge computing"):
  with members:
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "proactive edge computing in latency constrained fog networks"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)