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: "kernel belief propagation"
  Abstract: We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "pull message passing for nonparametric belief propagation"):
  with members:
    - "scene grammars factor graphs and belief propagation"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-3 (induced by source paper "learning general latent variable graphical models with predictive belief propagation"):
  with members:
    - "hilbert space embeddings of predictive state representations"
    - "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 "discriminative embeddings of latent variable models for structured data"):
  with members:
    - "gated graph sequence neural networks"
    - "deeply learning the messages in message passing inference"
    - "deep convolutional networks on graph structured data"
    - "conditional random fields as recurrent neural networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "convolutional neural networks over tree structures for programming language processing"
    - "learning deep structured models"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-5 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-6 (induced by source paper "provable bayesian inference via particle mirror descent"):
  with members:
    - "gaussian processes for big data"
    - "sparse stochastic inference for latent dirichlet allocation"
    - "nonparametric variational inference"

Connected through hyperedge HE-7 (induced by source paper "a kernel based nonparametric test for anomaly detection over line networks"):
  with members:
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-8 (induced by source paper "hilbert space embeddings of pomdps"):
  with members:
    - "modelling transition dynamics in mdps with rkhs embeddings"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "a kernel method for the two sample problem"
    - "perseus randomized point based value iteration for pomdps"
    - "value function approximations for partially observable markov decision processes"

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)