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: "bounded regret for finite armed structured bandits"
  Abstract: We study a new type of K-armed bandit problem where the expected return of one arm may depend on the returns of other arms. We present a new algorithm for this general class of problems and show that under certain circumstances it is possible to achieve finite expected cumulative regret. We also give problem-dependent lower bounds on the cumulative regret showing that at least in special cases the new algorithm is nearly optimal.

Connected through hyperedge HE-1 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "introduction to multi armed bandits"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-2 (induced by source paper "bounded regret for finitely parameterized multi armed bandits"):
  with members:
    - "cascading bandits for large scale recommendation problems"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "leveraging side observations in stochastic bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "bandit problems with side observations"

Connected through hyperedge HE-3 (induced by source paper "lexicographic multiarmed bandit"):
  with members:
    - "multi objective contextual multi armed bandit with a dominant objective"
    - "fairness in learning classic and contextual bandits"
    - "satisficing in multi armed bandit problems"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-4 (induced by source paper "global bandits"):
  with members:
    - "an information theoretic analysis of thompson sampling"
    - "distributed online learning via cooperative contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"
    - "an optimal algorithm for linear bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-5 (induced by source paper "optimal learning for dynamic coding in deadline constrained multi channel networks"):
  with members:
    - "risk sensitive reinforcement learning for urllc traffic in wireless networks"
    - "regret of queueing bandits"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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.LG(Machine Learning)