human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "delay and cooperation in nonstochastic bandits"):
  Abstract: We study networks of communicating learning agents that cooperate to solve a common nonstochastic bandit problem. Agents use an underlying communication network to get messages about actions selected by other agents, and drop messages that took more than $d$ hops to arrive, where $d$ is a delay parameter. We introduce \textsc{Exp3-Coop}, a cooperative version of the {\sc Exp3} algorithm and prove that with $K$ actions and $N$ agents the average per-agent regret after $T$ rounds is at most of order $\sqrt{\bigl(d+1 + \tfrac{K}{N}\alpha_{\le d}\bigr)(T\ln K)}$, where $\alpha_{\le d}$ is the independence number of the $d$-th power of the connected communication graph $G$. We then show that for any connected graph, for $d=\sqrt{K}$ the regret bound is $K^{1/4}\sqrt{T}$, strictly better than the minimax regret $\sqrt{KT}$ for noncooperating agents. More informed choices of $d$ lead to bounds which are arbitrarily close to the full information minimax regret $\sqrt{T\ln K}$ when $G$ is dense. When $G$ has sparse components, we show that a variant of \textsc{Exp3-Coop}, allowing agents to choose their parameters according to their centrality in $G$, strictly improves the regret. Finally, as a by-product of our analysis, we provide the first characterization of the minimax regret for bandit learning with delay.

Includes members:
  Member "explore no more improved high probability regret bounds for non stochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "multi player bandits a musical chairs approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "parallel correlation clustering on big graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair correlation clustering")
  Member "on distributed cooperative decision making in multiarmed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a decentralized communication policy for multi agent multi armed bandit problems")
  Member "nonstochastic multi armed bandits with graph structured feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "distributed online learning in social recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social learning in multi agent multi armed bandits")
  Member "online learning under delayed feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "logarithmic regret for adversarial online control")
  Member "distributed online learning via cooperative contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")

Question: Please tell me which class the source paper 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.LG(Machine Learning)