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: "mixed batches and symmetric discriminators for gan training"
  Abstract: Generative adversarial networks (GANs) are pow- erful generative models based on providing feed- back to a generative network via a discriminator network. However, the discriminator usually as- sesses individual samples. This prevents the dis- criminator from accessing global distributional statistics of generated samples, and often leads to mode dropping: the generator models only part of the target distribution. We propose to feed the discriminator with mixed batches of true and fake samples, and train it to predict the ratio of true samples in the batch. The latter score does not depend on the order of samples in a batch. Rather than learning this invariance, we introduce a generic permutation-invariant discriminator ar- chitecture. This architecture is provably a uni- versal approximator of all symmetric functions. Experimentally, our approach reduces mode col- lapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and CelebA datasets, both qualitatively and quantitatively.

Connected through hyperedge HE-1 (induced by source paper "improving generalization and stability of generative adversarial networks"):
  with members:
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "improved techniques for training gans"

Connected through hyperedge HE-2 (induced by source paper "gdpp learning diverse generations using determinantal point process"):
  with members:
    - "on gans and gmms"
    - "spectral normalization for generative adversarial networks"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "creating capsule wardrobes from fashion images"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "a simple baseline algorithm for graph classification"):
  with members:
    - "weighted spectral embedding of graphs"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "feature based classification of networks"

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)