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 policies with external memory"
  Abstract: In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend on the history of observations. In this paper, we explore a {\it stigmergic} approach, in which the agent's actions include the ability to set and clear bits in an external memory, and the external memory is included as part of the input to the agent. In this case, we need to learn a reactive policy in a highly non-Markovian domain. We explore two algorithms: SARSA(\lambda), which has had empirical success in partially observable domains, and VAPS, a new algorithm due to Baird and Moore, with convergence guarantees in partially observable domains. We compare the performance of these two algorithms on benchmark problems.

Connected through hyperedge HE-1 (induced by source paper "online cyber attack detection in smart grid a reinforcement learning approach"):
  with members:
    - "real time detection of hybrid and stealthy cyber attacks in smart grid"
    - "learning finite state controllers for partially observable environments"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning in pomdps with memoryless options and option observation initiation sets"):
  with members:
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "adam a method for stochastic optimization"
    - "learning finite state controllers for partially observable environments"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-3 (induced by source paper "learning deep neural network policies with continuous memory states"):
  with members:
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "learning contact rich manipulation skills with guided policy search"
    - "end to end training of deep visuomotor policies"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "learning finite state controllers for partially observable environments"
    - "hierarchical pomdp controller optimization by likelihood maximization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "memory based control with recurrent neural networks"):
  with members:
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"
    - "compatible value gradients for reinforcement learning of continuous deep policies"
    - "deep recurrent q learning for partially observable mdps"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning to cooperate via policy search"):
  with members:
    - "learning finite state controllers for partially observable environments"
    - "the complexity of decentralized control of markov decision processes"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "learning finite state controllers for partially observable environments"):
  with members:
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "solving pomdps by searching the space of finite policies"):
  with members:
    - "solving pomdps by searching in policy space"
    - "learning finite state controllers for partially observable environments"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-8 (induced by source paper "policy improvement for pomdps using normalized importance sampling"):
  with members:
    - "bounds on sample size for policy evaluation in markov environments"

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)