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: "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
  Abstract: This paper deals with the problem of efficient resource allocation in dynamic infrastructureless wireless networks. In a reactive interference-limited scenario, at each transmission trial, every transmitter selects a frequency channel from some common pool, together with a power level. As a result, for all transmitters, not only the fading gain, but the number and the power of interfering transmissions as well, vary over time. Due to the absence of a central controller and time varying network characteristics, it is highly inefficient for transmitters to acquire the global channel and network knowledge. Therefore, given no information, each transmitter selfishly intends to maximize its average reward, which is a function of the channel quality, as well as the joint selection profile of all transmitters. This scenario is modeled as an adversarial multiplayer multiarmed bandit game, where players attempt to minimize their so-called regret, while, at the network side, achieving equilibrium in some sense. Based on this model and to solve the resource allocation problem, in this paper, we develop two joint power level and channel selection strategies. We prove that the gap between the average rewards achieved by our approaches and that based on the best fixed strategy converges to zero asymptotically. Moreover, the empirical joint frequencies of the game converge to the set of correlated equilibria, which is characterized for two relaxed versions of the designed game.

Connected through hyperedge HE-1 (induced by source paper "ballooning multi armed bandits"):
  with members:
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "the myopia of crowds a study of collective evaluation on stack exchange"
    - "simple regret for infinitely many armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"):
  with members:
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "performance analysis of dynamic channel bonding in spatially distributed high density wlans"
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "ieee 802 11ax high efficiency wlans"
    - "multi player bandits a musical chairs approach"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "collaborative spatial reuse in wireless networks via selfish multi armed bandits"):
  with members:
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "breaking the interference barrier in dense wireless networks with interference alignment"
    - "ieee 802 11ax high efficiency wlans"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "optimal rate sampling in 802 11 systems"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "implications of decentralized q learning resource allocation in wireless networks"):
  with members:
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"

Connected through hyperedge HE-5 (induced by source paper "toward optimal adaptive wireless communications in unknown environments"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "almost optimal channel access in multi hop networks with unknown channel variables"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "decentralized online learning algorithms for opportunistic spectrum access"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-6 (induced by source paper "multi armed bandits with application to 5g small cells"):
  with members:
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "approximation algorithms for restless bandit problems"

Connected through hyperedge HE-7 (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"
    - "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"
    - "cooperative topology control with adaptation for improved lifetime in wireless sensor networks"

Connected through hyperedge HE-8 (induced by source paper "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"):
  with members:
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "adaptive power allocation and control in time varying multi carrier mimo networks"
    - "energy efficient precoder design for mimo ofdm with rate dependent circuit power"
    - "energy aware competitive power allocation for heterogeneous networks under qos constraints"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"

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)