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: "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
  Abstract: Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-optimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite. We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP.

Connected through hyperedge HE-1 (induced by source paper "varibad a very good method for bayes adaptive deep rl via meta learning"):
  with members:
    - "meta reinforcement learning with task embedding and shared policy"
    - "meta learning of sequential strategies"
    - "meta amortized variational inference and learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta reinforcement learning as task inference"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "neural predictive belief representations"):
  with members:
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to act by predicting the future"
    - "recurrent reinforcement learning a hybrid approach"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "compress and control"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "sparse adaptive dirichlet multinomial like processes"
    - "playing atari with deep reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "partition tree weighting"
    - "efficient tracking of large classes of experts"
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-4 (induced by source paper "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "near optimal brl using optimistic local transitions"
    - "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"
    - "decision theoretic coordination and control for active multi camera surveillance in uncertain partially observable environments"
    - "active markov information theoretic path planning for robotic environmental sensing"

Connected through hyperedge HE-5 (induced by source paper "efficient bayes adaptive reinforcement learning using sample based search"):
  with members:
    - "bandit algorithms for tree search"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "a bayesian sampling approach to exploration in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "pilot rollout and monte carlo tree search methods for job shop scheduling"):
  with members:
    - "satzilla portfolio based algorithm selection for sat"

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)