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: "two manifold problems with applications to nonlinear system identification"
  Abstract: Recently, there has been much interest in spectral approaches to learning manifolds---so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the same data. By solving these interconnected learning problems together, two-manifold algorithms are able to succeed where a non-integrated approach would fail: each view allows us to suppress noise in the other, reducing bias. We propose a class of algorithms for two-manifold problems, based on spectral decomposition of cross-covariance operators in Hilbert space, and discuss when two-manifold problems are useful. Finally, we demonstrate that solving a two-manifold problem can aid in learning a nonlinear dynamical system from limited data.

Connected through hyperedge HE-1 (induced by source paper "local canonical correlation analysis for nonlinear common variables discovery"):
  with members:
    - "multiview diffusion maps"
    - "nonparametric canonical correlation analysis"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-2 (induced by source paper "multimodal latent variable analysis"):
  with members:
    - "local canonical correlation analysis for nonlinear common variables discovery"
    - "multiview diffusion maps"
    - "nonparametric canonical correlation analysis"
    - "manifold relevance determination"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "kernel based sensor fusion with application to audio visual voice activity detection"):
  with members:
    - "multiview diffusion maps"
    - "nonparametric canonical correlation analysis"
    - "a filtering technique for markov chains with applications to spectral embedding"
    - "making laplacians commute"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "multi view kernel consensus for data analysis"):
  with members:
    - "multiview diffusion maps"
    - "a kernel method for canonical correlation analysis"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-5 (induced by source paper "multi view kernel consensus for data analysis and signal processing"):
  with members:
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-6 (induced by source paper "multiview diffusion maps"):
  with members:
    - "multi view kernel consensus for data analysis"
    - "kernel based sensor fusion with application to audio visual voice activity detection"

Connected through hyperedge HE-7 (induced by source paper "nonparametric canonical correlation analysis"):
  with members:
    - "sparse additive functional and kernel cca"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-8 (induced by source paper "hilbert space embeddings of predictive state representations"):
  with members:
    - "modelling transition dynamics in mdps with rkhs embeddings"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "closing the learning planning loop with predictive state representations"

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)