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: "hierarchical pomdp controller optimization by likelihood maximization"
  Abstract: Planning can often be simpli ed by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy discovery problem can be framed as a non-convex optimization problem. However, the inherent computational di culty of solving such an optimization problem makes it hard to scale to realworld problems. In another line of research, Toussaint et al. [18] developed a method to solve planning problems by maximumlikelihood estimation. In this paper, we show how the hierarchy discovery problem in partially observable domains can be tackled using a similar maximum likelihood approach. Our technique rst transforms the problem into a dynamic Bayesian network through which a hierarchical structure can naturally be discovered while optimizing the policy. Experimental results demonstrate that this approach scales better than previous techniques based on non-convex optimization.

Connected through hyperedge HE-1 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  with members:
    - "category level articulated object pose estimation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised discovery of parts structure and dynamics"
    - "inferring geometric constraints in human demonstrations"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "leveraging task knowledge for robot motion planning under uncertainty"):
  with members:
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "learning contact rich manipulation skills with guided policy search"

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"
    - "on the difficulty of training recurrent neural networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "anytime planning for decentralized pomdps using expectation maximization"):
  with members:
    - "the complexity of decentralized control of markov decision processes"
    - "heuristic search value iteration for pomdps"
    - "new inference strategies for solving markov decision processes using reversible jump mcmc"
    - "policy iteration for decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-5 (induced by source paper "new inference strategies for solving markov decision processes using reversible jump mcmc"):
  with members:
    - "pegasus a policy search method for large mdps and pomdps"

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)