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: "on training deep boltzmann machines"
  Abstract: The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.

Connected through hyperedge HE-1 (induced by source paper "applications of deep learning and reinforcement learning to biological data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "brain tumor segmentation with deep neural networks"
    - "deep reinforcement learning an overview"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning trends for focal brain pathology segmentation in mri"
    - "medical image denoising using convolutional denoising autoencoders"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "deepmirgene deep neural network based precursor microrna prediction"

Connected through hyperedge HE-2 (induced by source paper "representation learning a review and new perspectives"):
  with members:
    - "revisiting natural gradient for deep networks"
    - "a generative process for sampling contractive auto encoders"
    - "what regularized auto encoders learn from the data generating distribution"
    - "practical recommendations for gradient based training of deep architectures"
    - "better mixing via deep representations"
    - "multi column deep neural networks for image classification"
    - "shift invariance sparse coding for audio classification"
    - "implicit density estimation by local moment matching to sample from auto encoders"

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)