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: "rollout sampling approximate policy iteration"
  Abstract: Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "the limits and potentials of deep learning for robotics"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"

Connected through hyperedge HE-2 (induced by source paper "decision automation for electric power network recovery"):
  with members:
    - "optimal stochastic dynamic scheduling for managing community recovery from natural hazards"
    - "a modified approximate dynamic programming algorithm for community level food security following disasters"
    - "an approximate dynamic programming approach to food security of communities following hazards"
    - "accelerated gradient temporal difference learning"
    - "deep reinforcement learning in large discrete action spaces"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "approximate policy iteration for budgeted semantic video segmentation"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"
    - "speedmachines anytime structured prediction"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "learning states representations in pomdp"):
  with members:
    - "unsupervised model free representation learning"

Connected through hyperedge HE-5 (induced by source paper "rollout sampling policy iteration for decentralized pomdps"):
  with members:
    - "learning to cooperate via policy search"
    - "the complexity of decentralized control of markov decision processes"
    - "improved memory bounded dynamic programming for decentralized pomdps"

Connected through hyperedge HE-6 (induced by source paper "sparse reward processes"):
  with members:
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "complexity of stochastic branch and bound methods for belief tree search in bayesian reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "fast reinforcement learning with large action sets using error correcting output codes for mdp factorization"):
  with members:
    - "x armed bandits"
    - "solving multiclass learning problems via error correcting output codes"

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)