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: "multimodal transitions for generative stochastic networks"
  Abstract: Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distribution. The result of training is therefore a machine that generates samples through this Markov chain. However, the previously introduced GSN consistency theorems suggest that in order to capture a wide class of distributions, the transition operator in general should be multimodal, something that has not been done before this paper. We introduce for the first time multimodal transition distributions for GSNs, in particular using models in the NADE family (Neural Autoregressive Density Estimator) as output distributions of the transition operator. A NADE model is related to an RBM (and can thus model multimodal distributions) but its likelihood (and likelihood gradient) can be computed easily. The parameters of the NADE are obtained as a learned function of the previous state of the learned Markov chain. Experiments clearly illustrate the advantage of such multimodal transition distributions over unimodal GSNs.

Connected through hyperedge HE-1 (induced by source paper "gsns generative stochastic networks"):
  with members:
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "on invariance and selectivity in representation learning"
    - "deep haar scattering networks"
    - "nice non linear independent components estimation"
    - "how transferable are features in deep neural networks"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-2 (induced by source paper "how auto encoders could provide credit assignment in deep networks via target propagation"):
  with members:
    - "reweighted wake sleep"
    - "efficient gradient based inference through transformations between bayes nets and neural nets"
    - "deep generative stochastic networks trainable by backprop"
    - "deep learning of representations looking forward"
    - "generalized denoising auto encoders as generative models"
    - "deep autoregressive networks"
    - "what regularized auto encoders learn from the data generating distribution"
    - "distributed optimization of deeply nested systems"

Connected through hyperedge HE-3 (induced by source paper "generative class conditional autoencoders"):
  with members:
    - "going deeper with convolutions"
    - "conditional generative adversarial nets"
    - "deep generative stochastic networks trainable by backprop"
    - "multi digit number recognition from street view imagery using deep convolutional neural 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)