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: "gaussian binary restricted boltzmann machines for modeling natural image statistics"
  Abstract: We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of Gaussians, which gives a much better insight into the model's capabilities and limitations. We show that GRBMs are capable of learning meaningful features both in a two-dimensional blind source separation task and in modeling natural images. Further, we show that reported difficulties in training GRBMs are due to the failure of the training algorithm rather than the model itself. Based on our analysis we are able to propose several training recipes, which allowed successful and fast training in our experiments. Finally, we discuss the relationship of GRBMs to several modifications that have been proposed to improve the model.

Connected through hyperedge HE-1 (induced by source paper "learning tensor latent features"):
  with members:
    - "sustain scalable unsupervised scoring for tensors and its application to phenotyping"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-2 (induced by source paper "deterministic and generalized framework for unsupervised learning with restricted boltzmann machines"):
  with members:
    - "approximate message passing with restricted boltzmann machine priors"

Connected through hyperedge HE-3 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"):
  with members:
    - "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"
    - "theta rbm unfactored gated restricted boltzmann machine for rotation invariant representations"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "deep transductive semi supervised maximum margin clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "speech recognition with deep recurrent neural networks"
    - "modeling documents with deep boltzmann machines"
    - "conditional restricted boltzmann machines for structured output prediction"

Connected through hyperedge HE-4 (induced by source paper "shared latent subspace modelling within gaussian binary restricted boltzmann machines for nist i vector challenge 2014"):
  with members:
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"

Connected through hyperedge HE-5 (induced by source paper "scoring and classifying with gated auto encoders"):
  with members:
    - "going deeper with convolutions"
    - "deep generative stochastic networks trainable by backprop"
    - "conditional restricted boltzmann machines for structured output prediction"
    - "what regularized auto encoders learn from the data generating distribution"

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.NE(Neural and Evolutionary Computing)