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: "representation policy iteration"
  Abstract: This paper addresses a fundamental issue central to approximation methods for solving large Markov decision processes (MDPs): how to automatically learn the underlying representation for value function approximation? A novel theoretically rigorous framework is proposed that automatically generates geometrically customized orthonormal sets of basis functions, which can be used with any approximate MDP solver like least squares policy iteration (LSPI). The key innovation is a coordinate-free representation of value functions, using the theory of smooth functions on a Riemannian manifold. Hodge theory yields a constructive method for generating basis functions for approximating value functions based on the eigenfunctions of the self-adjoint (Laplace-Beltrami) operator on manifolds. In effect, this approach performs a global Fourier analysis on the state space graph to approximate value functions, where the basis functions reflect the largescale topology of the underlying state space. A new class of algorithms called Representation Policy Iteration (RPI) are presented that automatically learn both basis functions and approximately optimal policies. Illustrative experiments compare the performance of RPI with that of LSPI using two handcoded basis functions (RBF and polynomial state encodings).

Connected through hyperedge HE-1 (induced by source paper "manifold regularization for kernelized lstd"):
  with members:
    - "learning from conditional distributions via dual kernel embeddings"
    - "orthogonal random features"

Connected through hyperedge HE-2 (induced by source paper "learning efficient representations for reinforcement learning"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "spudd stochastic planning using decision diagrams"
    - "exploiting compositionality to explore a large space of model structures"
    - "efficient solution algorithms for factored mdps"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"):
  with members:
    - "practical kernel based reinforcement learning"
    - "galerkin methods for complementarity problems and variational inequalities"
    - "a dantzig selector approach to temporal difference learning"
    - "control design for markov chains under safety constraints a convex approach"
    - "sparse online learning via truncated gradient"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-4 (induced by source paper "predictive state temporal difference learning"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "planning in pomdps using multiplicity automata"
    - "reduced rank hidden markov models"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

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)