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: "satisficing in multi armed bandit problems"
  Abstract: Satisficing is a relaxation of maximizing and allows for less risky decision-making in the face of uncertainty. We propose two sets of satisficing objectives for the multi-armed bandit problem, where the objective is to achieve reward-based decision-making performance above a given threshold. We show that these new problems are equivalent to various standard multi-armed bandit problems with maximizing objectives and use the equivalence to find bounds on performance. The different objectives can result in qualitatively different behavior; for example, agents explore their options continually in one case and only a finite number of times in another. For the case of Gaussian rewards we show an additional equivalence between the two sets of satisficing objectives that allows algorithms developed for one set to be applied to the other. We then develop variants of the Upper Credible Limit (UCL) algorithm that solve the problems with satisficing objectives and show that these modified UCL algorithms achieve efficient satisficing performance.

Connected through hyperedge HE-1 (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"
    - "bounded regret for finite armed structured bandits"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-2 (induced by source paper "learning in restless multi armed bandits via adaptive arm sequencing rules"):
  with members:
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "distributed game theoretic optimization and management of multichannel aloha networks"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-3 (induced by source paper "guaranteed satisficing and finite regret analysis of a cognitive satisficing value function"):
  with members:
    - "efficient decision making by volume conserving physical object"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)