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: "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
  Abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for backscatter aided data offloading in mobile edge computing"):
  with members:
    - "on policy trust region policy optimisation with replay buffers"
    - "a deep reinforcement learning based approach for cost and energy aware multi flow mobile data offloading"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "decentralized computation offloading for multi user mobile edge computing a deep reinforcement learning approach"
    - "rainbow combining improvements in deep reinforcement learning"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "offloading and resource allocation with general task graph in mobile edge computing a deep reinforcement learning approach"):
  with members:
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "a survey on mobile edge computing the communication perspective"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "asynchronous methods for deep reinforcement learning"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"
    - "the energy frequency convexity rule modeling and experimental validation on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "fog computing based radio access networks issues and challenges"
    - "joint user association and resource allocation in virtualized wireless networks"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-5 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"

Connected through hyperedge HE-6 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-7 (induced by source paper "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "learning to entangle radio resources in vehicular communications an oblivious game theoretic perspective"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "deep learning with long short term memory for time series prediction"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "performance optimization in mobile edge computing via deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "machine learning in the air"
    - "deep reinforcement learning for resource management in network slicing"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "auto scaling network resources using machine learning to improve qos and reduce cost"
    - "addressing function approximation error in actor critic methods"
    - "deep learning in mobile and wireless networking a survey"
    - "comparative study of virtual machines and containers for devops developers"

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)