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: "distributed algorithms for learning and cognitive medium access with logarithmic regret"
  Abstract: The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users and sensing and access decisions are undertaken by them in a completely distributed manner. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the sum regret in distributed learning and access, which is the loss in secondary throughput due to learning and distributed access. For the scenario when the number of secondary users is known to the policy, we prove that the total regret is logarithmic in the number of transmission slots. This policy achieves order-optimal regret based on a logarithmic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated at each user through feedback. We propose a policy whose sum regret grows only slightly faster than logarithmic in the number of transmission slots.

Connected through hyperedge HE-1 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits"):
  with members:
    - "multi player bandits the adversarial case"
    - "competing bandits in matching markets"
    - "multi player multi armed bandits with non zero rewards on collisions for uncoordinated spectrum access"
    - "individual regret in cooperative nonstochastic multi armed bandits"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "social learning in multi agent multi armed bandits"
    - "multi player bandits a trekking approach"
    - "multi player multi armed bandits for stable allocation in heterogeneous ad hoc networks"

Connected through hyperedge HE-2 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits"):
  with members:
    - "competing bandits in matching markets"
    - "social learning in multi agent multi armed bandits"
    - "decentralized cooperative stochastic multi armed bandits"
    - "competing bandits learning under competition"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-3 (induced by source paper "selfish robustness and equilibria in multi player bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "competing bandits in matching markets"
    - "introduction to multi armed bandits"
    - "multiplayer bandits without observing collision information"
    - "dynamic pricing with finitely many unknown valuations"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "fast rates for bandit optimization with upper confidence frank wolfe"

Connected through hyperedge HE-4 (induced by source paper "coordination without communication optimal regret in two players multi armed bandits"):
  with members:
    - "multi player bandits the adversarial case"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "multiplayer bandits without observing collision information"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi armed bandit learning in iot networks learning helps even in non stationary settings"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"

Connected through hyperedge HE-5 (induced by source paper "decentralized multi player multi armed bandits with no collision information"):
  with members:
    - "multiplayer bandits without observing collision information"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"

Connected through hyperedge HE-6 (induced by source paper "social learning in multi agent multi armed bandits"):
  with members:
    - "collaboratively learning the best option on graphs using bounded local memory"
    - "decentralized cooperative stochastic multi armed bandits"
    - "a distributed learning dynamics in social groups"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "delay and cooperation in nonstochastic bandits"
    - "human collective intelligence as distributed bayesian inference"
    - "collaborative filtering bandits"

Connected through hyperedge HE-7 (induced by source paper "distributed learning for channel allocation over a shared spectrum"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "game of thrones fully distributed learning for multi player bandits"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-8 (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"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"

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.NI(Networking and Internet Architecture)