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: "an adaptive algorithm for finite stochastic partial monitoring"
  Abstract: We present a new anytime algorithm that achieves near-optimal regret for any instance of finite stochastic partial monitoring. In particular, the new algorithm achieves the minimax regret, within logarithmic factors, for both "easy" and "hard" problems. For easy problems, it additionally achieves logarithmic individual regret. Most importantly, the algorithm is adaptive in the sense that if the opponent strategy is in an "easy region" of the strategy space then the regret grows as if the problem was easy. As an implication, we show that under some reasonable additional assumptions, the algorithm enjoys an O(\sqrt{T}) regret in Dynamic Pricing, proven to be hard by Bartok et al. (2011).

Connected through hyperedge HE-1 (induced by source paper "dcm bandits learning to rank with multiple clicks"):
  with members:
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "efficient learning in large scale combinatorial semi bandits"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "online influence maximization under independent cascade model with semi bandit feedback"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "model independent online learning for influence maximization"
    - "cascading bandits for large scale recommendation problems"
    - "information gathering in networks via active exploration"
    - "cascading bandits learning to rank in the cascade model"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

Connected through hyperedge HE-3 (induced by source paper "cascading bandits learning to rank in the cascade model"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "learning to act greedily polymatroid semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "efficient learning in large scale combinatorial semi bandits"
    - "leveraging side observations in stochastic bandits"
    - "from bandits to experts on the value of side observations"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "copeland dueling bandits"):
  with members:
    - "contextual dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "x armed bandits"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "reducing dueling bandits to cardinal bandits"):
  with members:
    - "decoupling exploration and exploitation in multi armed bandits"
    - "active learning using smooth relative regret approximations with applications"

Connected through hyperedge HE-6 (induced by source paper "relative upper confidence bound for the k armed dueling bandit problem"):
  with members:
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"

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)