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: "calibrating energy based generative adversarial networks"
  Abstract: In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples.Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. We derive the analytic form of the induced solution, and analyze the properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution.

Connected through hyperedge HE-1 (induced by source paper "adversarial distributional training for robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "a direct approach to robust deep learning using adversarial networks"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"

Connected through hyperedge HE-2 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial example detection and classification with asymmetrical adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "conditional generative models are not robust"
    - "natural and adversarial error detection using invariance to image transformations"
    - "consistency regularization for generative adversarial networks"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "towards the first adversarially robust neural network model on mnist"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on scalable and efficient computation of large scale optimal transport"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "spectral normalization for generative adversarial networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-6 (induced by source paper "conditional transferring features scaling gans to thousands of classes with 30 less high quality data for training"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "progressive growing of gans for improved quality stability and variation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "anti makeup learning a bi level adversarial network for makeup invariant face verification"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy generators for energy based models"):
  with members:
    - "efficient gan based anomaly detection"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-8 (induced by source paper "stein bridging enabling mutual reinforcement between explicit and implicit generative models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "a variational inequality perspective on generative adversarial nets"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "learning implicit generative models by teaching explicit ones"
    - "large scale gan training for high fidelity natural image synthesis"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "wasserstein distributional robustness and regularization in statistical learning"

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)