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 in pomdps with monte carlo tree search"
  Abstract: The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observable Markov Decision Processes (BA-POMDPs) extend POMDPs to allow the model to be learned during execution. BA-POMDPs are a Bayesian RL approach that, in principle, allows for an optimal trade-off between exploitation and exploration. Unfortunately, BA-POMDPs are currently impractical to solve for any non-trivial domain. In this paper, we extend the Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting method, which we call BA-POMCP, is able to tackle problems that previous solution methods have been unable to solve. Additionally, we introduce several techniques that exploit the BA-POMDP structure to improve the efficiency of BA-POMCP along with proof of their convergence.

Connected through hyperedge HE-1 (induced by source paper "integrating acting planning and learning in hierarchical operational models"):
  with members:
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "towards deep symbolic reinforcement learning"
    - "learning of behavior trees for autonomous agents"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "active perception in adversarial scenarios using maximum entropy deep reinforcement learning"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "regret minimization for partially observable deep reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "reinforcement learning with deep energy based policies"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "qmdp net deep learning for planning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-3 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "information theoretic measures of influence based on content dynamics"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch"):
  with members:
    - "bayesian reinforcement learning in factored pomdps"
    - "despot online pomdp planning with regularization"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "learning to make predictions in partially observable environments without a generative model"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-5 (induced by source paper "incremental learning of discrete planning domains from continuous perceptions"):
  with members:
    - "learning plannable representations with causal infogan"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "pomdp model learning for human robot collaboration"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "learning symbolic models of stochastic domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "online learning and planning in partially observable domains without prior knowledge"):
  with members:
    - "combining offline models and online monte carlo tree search for planning from scratch"
    - "bayesian reinforcement learning in factored pomdps"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-7 (induced by source paper "deep variational reinforcement learning for pomdps"):
  with members:
    - "filtering variational objectives"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "qmdp net deep learning for planning under partial observability"
    - "asynchronous methods for deep reinforcement learning"
    - "value iteration networks"
    - "playing fps games with deep reinforcement learning"
    - "importance weighted autoencoders"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-8 (induced by source paper "bayesian reinforcement learning in factored pomdps"):
  with members:
    - "deep variational reinforcement learning for pomdps"
    - "efficient exploration through bayesian deep q networks"
    - "on improving deep reinforcement learning for pomdps"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "approximate planning for factored pomdps using belief state simplification"
    - "playing atari with deep reinforcement learning"

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)