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: "deep generative stochastic networks trainable by backprop"
  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. The transition distribution of the Markov chain is conditional on the previous state, generally involving a small move, so this conditional distribution has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn because it is easier to approximate its partition function, more like learning to perform supervised function approximation, with gradients that can be obtained by backprop. We provide theorems that generalize recent work on the probabilistic interpretation of denoising autoencoders and obtain along the way an interesting justification for dependency networks and generalized pseudolikelihood, along with a definition of an appropriate joint distribution and sampling mechanism even when the conditionals are not consistent. GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. We validate these theoretical results with experiments on two image datasets using an architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with simple backprop, without the need for layerwise pretraining.

Connected through hyperedge HE-1 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders"):
  with members:
    - "tackling over pruning in variational autoencoders"
    - "vae with a vampprior"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "deep feature consistent variational autoencoder"
    - "wavenet a generative model for raw audio"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-2 (induced by source paper "on educating machines"):
  with members:
    - "machine teaching in hierarchical genetic reinforcement learning curriculum design of reward functions for swarm shepherding"
    - "an overview of machine teaching"
    - "progressive neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "a multi disciplinary review of knowledge acquisition methods"
    - "computational red teaming in a sudoku solving context neural network based skill representation and acquisition"
    - "a review of theoretical and practical challenges of trusted autonomy in big data"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification"):
  with members:
    - "deep image super resolution via natural image priors"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "dynamic routing between capsules"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "discriminative embeddings of latent variable models for structured data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "neural variational inference for text processing"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "reconstruction of hidden representation for robust feature extraction"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "deconstructing the ladder network architecture"
    - "made masked autoencoder for distribution estimation"
    - "semi supervised learning with ladder networks"
    - "rectified factor networks"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "deep learning of representations looking forward"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-5 (induced by source paper "gradient descent based weight learning for grouping problems application on graph coloring and equitable graph coloring"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "a dsatur based algorithm for the equitable coloring problem"
    - "adam a method for stochastic optimization"
    - "variations on memetic algorithms for graph coloring problems"
    - "a tabu search heuristic for the equitable coloring problem"
    - "recent advances in graph partitioning"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "breaking instance independent symmetries in exact graph coloring"

Connected through hyperedge HE-6 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "neural joint source channel coding"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "discrete autoencoders for sequence models"
    - "theory and experiments on vector quantized autoencoders"
    - "deep learning for joint source channel coding of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "learning implicit fields for generative shape modeling"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multiresolution tree networks for 3d point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "scores shape composition with recursive substructure priors"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "occupancy networks learning 3d reconstruction in function space"

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)