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: "adaptive channel allocation spectrum etiquette for cognitive radio networks"
  Abstract: In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. #R##N#We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

Connected through hyperedge HE-1 (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"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sas assisted coexistence aware dynamic channel assignment in cbrs band"):
  with members:
    - "a variant of the maximum weight independent set problem"
    - "capacity considerations for secondary networks in tv white space"
    - "approximation algorithms for secondary spectrum auctions"

Connected through hyperedge HE-3 (induced by source paper "location privacy in cognitive radio networks a survey"):
  with members:
    - "preserving the location privacy of secondary users in cooperative spectrum sensing"
    - "an efficient technique for protecting location privacy of cooperative spectrum sensing users"
    - "securefind secure and privacy preserving object finding via mobile crowdsourcing"
    - "lpos location privacy for optimal sensing in cognitive radio networks"
    - "geo indistinguishability differential privacy for location based systems"
    - "security of spectrum learning in cognitive radios"
    - "channel assignment with access contention resolution for cognitive radio networks"
    - "towards practical oblivious ram"

Connected through hyperedge HE-4 (induced by source paper "on the exponential rate of convergence of fictitious play in potential games"):
  with members:
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "cooperative topology control with adaptation for improved lifetime in wireless sensor networks"
    - "learning equilibria with partial information in decentralized wireless networks"

Connected through hyperedge HE-5 (induced by source paper "a survey on applications of model free strategy learning in cognitive wireless networks"):
  with members:
    - "tact a transfer actor critic learning framework for energy saving in cellular radio access networks"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "power allocation with stackelberg game in femtocell networks a self learning approach"
    - "online reinforcement learning for dynamic multimedia systems"
    - "dynamic conjectures in random access networks using bio inspired learning"
    - "accelerating reinforcement learning through implicit imitation"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive survey of potential game approaches to wireless networks"):
  with members:
    - "centralized distributed spectrum access for small cell networks a cloud based game solution"
    - "decentralized computation offloading game for mobile cloud computing"
    - "database assisted spectrum access in dynamic networks a distributed learning solution"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "distributed learning algorithms for spectrum sharing in spatial random access wireless networks"
    - "spatial spectrum access game"
    - "joint access point selection and power allocation for uplink wireless networks"
    - "database assisted distributed spectrum sharing"

Connected through hyperedge HE-7 (induced by source paper "dynamic spectrum access in time varying environment distributed learning beyond expectation optimization"):
  with members:
    - "a game theoretic perspective on self organizing optimization for cognitive small cells"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"
    - "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"

Connected through hyperedge HE-8 (induced by source paper "dynamic spectrum access with statistical qos provisioning a distributed learning approach beyond expectation optimization"):
  with members:
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"
    - "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"

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)