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: "bandit models of human behavior reward processing in mental disorders"
  Abstract: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. We demonstrate empirically that the proposed parametric approach can often outperform the baseline Thompson Sampling on a variety of datasets. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions.

Connected through hyperedge HE-1 (induced by source paper "a survey on practical applications of multi armed and contextual bandits"):
  with members:
    - "interpretable multi objective reinforcement learning through policy orchestration"
    - "low rank bandit methods for high dimensional dynamic pricing"
    - "context attentive bandits contextual bandit with restricted context"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "algorithm selection as a bandit problem with unbounded losses"

Connected through hyperedge HE-2 (induced by source paper "a story of two streams reinforcement learning models from human behavior and neuropsychiatry"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "split q learning reinforcement learning with two stream rewards"

Connected through hyperedge HE-3 (induced by source paper "split q learning reinforcement learning with two stream rewards"):
  with members:
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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.AI(Artificial Intelligence)