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: "performance and convergence of multi user online learning"
  Abstract: We study the problem of allocating multiple users to a set of wireless channels in a decentralized manner when the channel quali- ties are time-varying and unknown to the users, and accessing the same channel by multiple users leads to reduced quality due to interference. In such a setting the users not only need to learn the inherent channel quality and at the same time the best allocations of users to channels so as to maximize the social welfare. Assuming that the users adopt a certain online learning algorithm, we investigate under what conditions the socially optimal allocation is achievable. In particular we examine the effect of different levels of knowledge the users may have and the amount of communications and cooperation. The general conclusion is that when the cooperation of users decreases and the uncertainty about channel payoffs increases it becomes harder to achieve the socially opti- mal allocation.

Connected through hyperedge HE-1 (induced by source paper "periodic bandits and wireless network selection"):
  with members:
    - "cooperation speeds surfing use co bandit"
    - "congestion aware distributed network selection for integrated cellular and wi fi networks"
    - "shrewd selection speeds surfing use smart exp3"
    - "efficient algorithms for adversarial contextual learning"
    - "multi user lax communications a multi armed bandit approach"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online distributed sensor selection"

Connected through hyperedge HE-2 (induced by source paper "cooperation speeds surfing use co bandit"):
  with members:
    - "congestion aware distributed network selection for integrated cellular and wi fi networks"
    - "delay and cooperation in nonstochastic bandits"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "shrewd selection speeds surfing use smart exp3"):
  with members:
    - "congestion aware distributed network selection for integrated cellular and wi fi networks"
    - "online distributed sensor selection"

Connected through hyperedge HE-4 (induced by source paper "an online approach to dynamic channel access and transmission scheduling"):
  with members:
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework"
    - "jamming resistant learning in wireless networks"
    - "to stay or to switch multiuser dynamic channel access"
    - "on a game theoretic approach to capacity maximization in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "distributed online big data classification using context information"):
  with members:
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "distributed learning in wireless sensor networks"

Connected through hyperedge HE-6 (induced by source paper "joint opportunistic scheduling and selective channel feedback"):
  with members:
    - "smart scheduling and feedback allocation over non stationary wireless channels"
    - "scheduling with rate adaptation under incomplete knowledge of channel estimator statistics"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "rethinking information theory for mobile ad hoc networks"

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.MA(Multiagent Systems)