human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans"):
  Abstract: GANs for time series data often use sliding windows or self-attention to capture underlying time dependencies. While these techniques have no clear theoretical justification, they are successful in significantly reducing the discriminator size, speeding up the training process, and improving the generation quality. In this paper, we provide both theoretical foundations and a practical framework of GANs for high-dimensional distributions with conditional independence structure captured by a Bayesian network, such as time series data. We prove that several probability divergences satisfy subadditivity properties with respect to the neighborhoods of the Bayes-net graph, providing an upper bound on the distance between two Bayes-nets by the sum of (local) distances between their marginals on every neighborhood of the graph. This leads to our proposed Subadditive GAN framework that uses a set of simple discriminators on the neighborhoods of the Bayes-net, rather than a giant discriminator on the entire network, providing significant statistical and computational benefits. We show that several probability distances including Jensen-Shannon, Total Variation, and Wasserstein, have subadditivity or generalized subadditivity. Moreover, we prove that Integral Probability Metrics (IPMs), which encompass commonly-used loss functions in GANs, also enjoy a notion of subadditivity under some mild conditions. Furthermore, we prove that nearly all f-divergences satisfy local subadditivity in which subadditivity holds when the distributions are relatively close. Our experiments on synthetic as well as real-world datasets verify the proposed theory and the benefits of subadditive GANs.

Includes members:
  Member "naomi non autoregressive multiresolution sequence imputation":
    also connected through other hyperedges:
      (none)
  Member "mad gan multivariate anomaly detection for time series data with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "adversarial video generation on complex datasets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "gansynth adversarial neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "adversarial audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ddsp differentiable digital signal processing")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "on the discrimination generalization tradeoff in gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "square hellinger subadditivity for bayesian networks and its applications to identity testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")

Question: Please tell me which class the source paper 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)