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: "dynamic computation offloading for mobile edge computing with energy harvesting devices"
  Abstract: Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The  execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the  Lyapunov optimization-based dynamic computation offloading  algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "neuromemristive circuits for edge computing a review"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "learning in memristive neural network architectures using analog backpropagation circuits"
    - "neuron inspired data encoding memristive multi level memory cell"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "memristive lstm network hardware architecture for time series predictive modeling problem"
    - "approximate probabilistic neural networks with gated threshold logic"

Connected through hyperedge HE-2 (induced by source paper "analysis on computation intensive status update in mobile edge computing"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "age of information for computation intensive messages in mobile edge computing"
    - "timely cloud computing preemption and waiting"
    - "age of information in a network of preemptive servers"
    - "the age of information in networks moments distributions and sampling"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-3 (induced by source paper "communications caching computing tradeoff analysis for bidirectional data computation in mobile edge networks"):
  with members:
    - "bandwidth gain from mobile edge computing and caching in wireless multicast systems"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint pushing and caching for bandwidth utilization maximization in wireless networks"
    - "the three primary colors of mobile systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "power delay tradeoff in multi user mobile edge computing systems"

Connected through hyperedge HE-4 (induced by source paper "mobile edge intelligence and computing for the internet of vehicles"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "client edge cloud hierarchical federated learning"
    - "a mobility aware vehicular caching scheme in content centric networks model and optimization"
    - "edge assisted hierarchical federated learning with non iid data"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "federated learning strategies for improving communication efficiency"
    - "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-5 (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-6 (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"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-7 (induced by source paper "bandwidth gain from mobile edge computing and caching in wireless multicast systems"):
  with members:
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "modeling and trade off for mobile communication computing and caching networks"
    - "toward low latency and ultra reliable virtual reality"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "joint pushing and caching for bandwidth utilization maximization in wireless networks"

Connected through hyperedge HE-8 (induced by source paper "coded federated computing in wireless networks with straggling devices and imperfect csi"):
  with members:
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "a storage computation communication tradeoff for distributed computing"
    - "wireless data center networks advances challenges and opportunities"
    - "improved latency communication trade off for map shuffle reduce systems with stragglers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "wireless mapreduce distributed computing"

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.IT(Information Theory)