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: "distributional adversarial networks"
  Abstract: We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose two such distributional adversaries that operate and predict on samples, and show how they can be easily implemented on top of existing models. Various experimental results show that generators trained with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with pointwise prediction discriminators. The application of our framework to domain adaptation also results in considerable improvement over recent state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "spatial evolutionary generative adversarial networks"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "evolutionary generative adversarial networks"
    - "towards distributed coevolutionary gans"
    - "lipizzaner a system that scales robust generative adversarial network training"
    - "dual discriminator generative adversarial nets"
    - "dual motion gan for future flow embedded video prediction"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "dist gan an improved gan using distance constraints"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "selective deep convolutional features for image retrieval"
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"
    - "energy based generative adversarial network"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "lipizzaner a system that scales robust generative adversarial network training"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "do gans actually learn the distribution an empirical study"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "ensembles of generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "inception score label smoothing gradient vanishing and log d x alternative"):
  with members:
    - "activation maximization generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "neural photo editing with introspective adversarial networks"
    - "stacked generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "tripletgan training generative model with triplet loss"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "training triplet networks with gan"
    - "mmd gan towards deeper understanding of moment matching network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "energy based generative adversarial network"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

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)