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: "hilbert space embeddings of predictive state representations"
  Abstract: Predictive State Representations (PSRs) are an expressive class of models for controlled stochastic processes. PSRs represent state as a set of predictions of future observable events. Because PSRs are defined entirely in terms of observable data, statistically consistent estimates of PSR parameters can be learned efficiently by manipulating moments of observed training data. Most learning algorithms for PSRs have assumed that actions and observations are finite with low cardinality. In this paper, we generalize PSRs to infinite sets of observations and actions, using the recent concept of Hilbert space embeddings of distributions. The essence is to represent the state as a nonparametric conditional embedding operator in a Reproducing Kernel Hilbert Space (RKHS) and leverage recent work in kernel methods to estimate, predict, and update the representation. We show that these Hilbert space embeddings of PSRs are able to gracefully handle continuous actions and observations, and that our learned models outperform competing system identification algorithms on several prediction benchmarks.

Connected through hyperedge HE-1 (induced by source paper "learning causal state representations of partially observable environments"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "deep variational reinforcement learning for pomdps"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "rule extraction algorithm for deep neural networks a review"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-2 (induced by source paper "learning general latent variable graphical models with predictive belief propagation"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent junction trees"
    - "kernel belief propagation"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "learning general latent variable graphical models with predictive belief propagation and hilbert space embeddings"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent junction trees"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "spectral methods for nonparametric models"):
  with members:
    - "a tensor approach to learning mixed membership community models"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "spectral methods for learning multivariate latent tree structure"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "hierarchically compositional kernels for scalable nonparametric learning"):
  with members:
    - "an n log n parallel fast direct solver for kernel matrices"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-6 (induced by source paper "efficient learning and planning with compressed predictive states"):
  with members:
    - "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"
    - "predictive state temporal difference learning"
    - "feature hashing for large scale multitask learning"

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)