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: "gsns generative stochastic networks"
  Abstract: We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. Because the transition distribution is a conditional distribution generally involving a small move, it has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn, more like learning to perform supervised function approximation, with gradients that can be obtained by back-propagation. The theorems provided here generalize recent work on the probabilistic interpretation of denoising auto-encoders and provide an interesting justification for dependency networks and generalized pseudolikelihood (along with defining an appropriate joint distribution and sampling mechanism, even when the conditionals are not consistent). We study how GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. Successful experiments are conducted, validating these theoretical results, on two image datasets and with a particular architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with backprop, without the need for layerwise pretraining.

Connected through hyperedge HE-1 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "sliced score matching a scalable approach to density and score estimation"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "discrete object generation with reversible inductive construction"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "combining learned representations for combinatorial optimization"):
  with members:
    - "voltage driven building block for hardware belief networks"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "neural turing machines"
    - "learning to execute"

Connected through hyperedge HE-4 (induced by source paper "transport analysis of infinitely deep neural network"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "policy optimization as wasserstein gradient flows"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "implicit regularization in deep learning"
    - "neural network with unbounded activation functions is universal approximator"

Connected through hyperedge HE-5 (induced by source paper "unrolled generative adversarial networks"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "density estimation using real nvp"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "energy based generative adversarial network"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-6 (induced by source paper "conservativeness of untied auto encoders"):
  with members:
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "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.LG(Machine Learning)