human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "self learning local supervision encoding framework to constrict and disperse feature distribution for clustering"):
  Abstract: To obtain suitable feature distribution is a difficult task in machine learning, especially for unsupervised learning. In this paper, we propose a novel self-learning local supervision encoding framework based on RBMs, in which the self-learning local supervisions from visible layer are integrated into the contrastive divergence (CD) learning of RBMs to constrict and disperse the distribution of the hidden layer features for clustering tasks. In the framework, we use sigmoid transformation to obtain hidden layer and reconstructed hidden layer features from visible layer and reconstructed visible layer units during sampling procedure. The self-learning local supervisions contain local credible clusters which stem from different unsupervised learning and unanimous voting strategy. They are fused into hidden layer features and reconstructed hidden layer features. For the same local clusters, the hidden features and reconstructed hidden layer features of the framework tend to constrict together. Furthermore, the hidden layer features of different local clusters tend to disperse in the encoding process. Under such framework, we present two instantiation models with the reconstruction of two different visible layers. One is self-learning local supervision GRBM (slsGRBM) model with Gaussian linear visible units and binary hidden units using linear transformation for visible layer reconstruction. The other is self-learning local supervision RBM (slsRBM) model with binary visible and hidden units using sigmoid transformation for visible layer reconstruction.

Includes members:
  Member "restricted boltzmann machines with gaussian visible units guided by pairwise constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minor constraint disturbances for deep semi supervised learning")
  Member "label free supervision of neural networks with physics and domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plug and play rescaling based crowd counting in static images")
  Member "deep transductive semi supervised maximum margin clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints")
  Member "a generative restricted boltzmann machine based method for high dimensional motion data modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational conditional dependence hidden markov models for human action recognition")
  Member "learning structured ordinal measures for video based face recognition":
    also connected through other hyperedges:
      (none)
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

Question: Please tell me which class the source paper 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)