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: "sparse boltzmann machines with structure learning as applied to text analysis"
  Abstract: We are interested in exploring the possibility and benefits of structure learning for deep models. As the first step, this paper investigates the matter for Restricted Boltzmann Machines (RBMs). We conduct the study with Replicated Softmax, a variant of RBMs for unsupervised text analysis. We present a method for learning what we call Sparse Boltzmann Machines, where each hidden unit is connected to a subset of the visible units instead of all of them. Empirical results show that the method yields models with significantly improved model fit and interpretability as compared with RBMs where each hidden unit is connected to all visible units.

Connected through hyperedge HE-1 (induced by source paper "learning sparse deep feedforward networks via tree skeleton expansion"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "neural architecture search with reinforcement learning"
    - "learning both weights and connections for efficient neural networks"
    - "data free parameter pruning for deep neural networks"
    - "progressive em for latent tree models and hierarchical topic detection"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "building sparse deep feedforward networks using tree receptive fields"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "learning both weights and connections for efficient neural networks"
    - "data free parameter pruning for deep neural networks"
    - "adam a method for stochastic optimization"
    - "memory bounded deep convolutional networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "a novel document generation process for topic detection based on hierarchical latent tree models"):
  with members:
    - "progressive em for latent tree models and hierarchical topic detection"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "latent tree models for hierarchical topic detection"):
  with members:
    - "topic browsing for research papers with hierarchical latent tree analysis"
    - "discovering structure in high dimensional data through correlation explanation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a survey on latent tree models and applications"
    - "a method of moments for mixture models and hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "latent tree analysis"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "identification and classification of tcm syndrome types among patients with vascular mild cognitive impairment using latent tree analysis"
    - "learning both weights and connections for efficient neural networks"
    - "progressive em for latent tree models and hierarchical topic detection"
    - "a survey on latent tree models and applications"
    - "latent tree models and approximate inference in bayesian networks"

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)