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: "game theory for multi access edge computing survey use cases and future trends"
  Abstract: Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game’s usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game’s model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"
    - "fundamental concepts of cyber resilience introduction and overview"

Connected through hyperedge HE-2 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"
    - "applications of deep reinforcement learning in communications and networking a survey"

Connected through hyperedge HE-3 (induced by source paper "computation offloading and content caching delivery in vehicular edge computing a survey"):
  with members:
    - "spectrum resource management for multi access edge computing in autonomous vehicular networks"
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "concurrent transmission scheduling for perceptual data sharing in mmwave vehicular networks"
    - "vehicular edge computing via deep reinforcement learning"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "joint offloading and resource allocation in vehicular edge computing and networks"
    - "millimeter wave v2v communications distributed association and beam alignment"

Connected through hyperedge HE-4 (induced by source paper "differential privacy in blockhain technology a futuristic approach"):
  with members:
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "security services using blockchains a state of the art survey"
    - "untangling blockchain a data processing view of blockchain 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.GT(Computer Science and Game Theory)