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: "predictive state temporal difference learning"
  Abstract: We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.

Connected through hyperedge HE-1 (induced by source paper "learning to filter with predictive state inference machines"):
  with members:
    - "deeply learning the messages in message passing inference"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"
    - "stability conditions for online learnability"
    - "optimistic rates for learning with a smooth loss"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "learning nonlinear dynamic models"

Connected through hyperedge HE-2 (induced by source paper "efficient learning and planning with compressed predictive states"):
  with members:
    - "hilbert space embeddings of predictive state representations"
    - "pegasus a policy search method for large mdps and pomdps"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "local loss optimization in operator models a new insight into spectral learning"
    - "a spectral learning approach to range only slam"
    - "a method of moments for mixture models and hidden markov models"
    - "bellman error based feature generation using random projections on sparse spaces"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "distributed policy evaluation under multiple behavior strategies"):
  with members:
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "multi timescale nexting in a reinforcement learning robot"

Connected through hyperedge HE-4 (induced by source paper "a spectral learning approach to range only slam"):
  with members:
    - "reduced rank hidden markov models"
    - "closing the learning planning loop with predictive state representations"
    - "matrix completion with noise"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "two manifold problems with applications to nonlinear system identification"):
  with members:
    - "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.LG(Machine Learning)