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: "implicit generation and generalization in energy based models"
  Abstract: Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. Finally, we show that EBMs are useful models across a wide variety of tasks, achieving state-of-the-art out-of-distribution classification, adversarially robust classification, state-of-the-art continual online class learning, and coherent long term predicted trajectory rollouts.

Connected through hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "on the expressive power of deep polynomial neural networks"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "residual networks of residual networks multilevel residual networks"
    - "mixed link networks"

Connected through hyperedge HE-2 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "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"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "pixelvae improved pixelvae with discrete prior"):
  with members:
    - "gumbolt extending gumbel trick to boltzmann priors"
    - "deep visual domain adaptation a survey"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "prototypical networks for few shot learning"
    - "fixing a broken elbo"
    - "vae with a vampprior"
    - "pixelsnail an improved autoregressive generative model"

Connected through hyperedge HE-5 (induced by source paper "real or fake learning to discriminate machine from human generated text"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "blockwise parallel decoding for deep autoregressive models"

Connected through hyperedge HE-6 (induced by source paper "adversarial fisher vectors for unsupervised representation learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "task2vec task embedding for meta learning"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-7 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "importance weighting and variational inference"
    - "kernel exponential family estimation via doubly dual embedding"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-8 (induced by source paper "your classifier is secretly an energy based model and you should treat it like one"):
  with members:
    - "conditional generative models are not robust"
    - "generative modeling by estimating gradients of the data distribution"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"
    - "adversarial distillation of bayesian neural network posteriors"

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)