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: "algorithm selection as a bandit problem with unbounded losses"
  Abstract: Algorithm selection is typically based on models of algorithm performance, learned during a separate offline training sequence, which can be prohibitively expensive. In recent work, we adopted an online approach, in which a performance model is iteratively updated and used to guide selection on a sequence of problem instances. The resulting exploration-exploitation trade-off was represented as a bandit problem with expert advice, using an existing solver for this game, but this required the setting of an arbitrary bound on algorithm runtimes, thus invalidating the optimal regret of the solver. In this paper, we propose a simpler framework for representing algorithm selection as a bandit problem, with partial information, and an unknown bound on losses. We adapt an existing solver to this game, proving a bound on its expected regret, which holds also for the resulting algorithm selection technique. We present preliminary experiments with a set of SAT solvers on a mixed SAT-UNSAT benchmark.

Connected through hyperedge HE-1 (induced by source paper "data efficient training for reinforcement learning with adaptive behavior policy sharing"):
  with members:
    - "a generalized framework for population based training"
    - "a distributional perspective on reinforcement learning"
    - "population based training of neural networks"
    - "deep exploration via bootstrapped dqn"
    - "asynchronous methods for deep reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "trust region policy optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "bandit models of human behavior reward processing in mental disorders"
    - "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"

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)