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: "blind construction of optimal nonlinear recursive predictors for discrete sequences"
  Abstract: We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variablelength Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.

Connected through hyperedge HE-1 (induced by source paper "identifying cover songs using information theoretic measures of similarity"):
  with members:
    - "analyzing worms and network traffic using compression"
    - "shannon information and kolmogorov complexity"
    - "on prediction using variable order markov models"
    - "the similarity metric"

Connected through hyperedge HE-2 (induced by source paper "understanding the predictive power of computational mechanics and echo state networks in social media"):
  with members:
    - "latent self exciting point process model for spatial temporal networks"
    - "collective phenomena and non finite state computation in a human social system"
    - "information transfer in social media"

Connected through hyperedge HE-3 (induced by source paper "learning to make predictions in partially observable environments without a generative model"):
  with members:
    - "temporal difference networks"
    - "the optimal reward baseline for gradient based reinforcement learning"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "predictive linear gaussian models of stochastic dynamical systems"
    - "closing the learning planning loop with predictive state representations"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "pattern classification in symbolic streams via semantic annihilation of information"):
  with members:
    - "an algorithm for pattern discovery in time series"

Connected through hyperedge HE-5 (induced by source paper "learning hidden markov models using non negative matrix factorization"):
  with members:
    - "on the complexity of nonnegative matrix factorization"

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)