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 "one element batch training by moving window"):
  Abstract: Several deep models, esp. the generative, compare the samples from two distributions (e.g. WAE like AutoEncoder models, set-processing deep networks, etc) in their cost functions. Using all these methods one cannot train the model directly taking small size (in extreme -- one element) batches, due to the fact that samples are to be compared. #R##N#We propose a generic approach to training such models using one-element mini-batches. The idea is based on splitting the batch in latent into parts: previous, i.e. historical, elements used for latent space distribution matching and the current ones, used both for latent distribution computation and the minimization process. Due to the smaller memory requirements, this allows to train networks on higher resolution images then in the classical approach.

Includes members:
  Member "sliced generative models":
    also connected through other hyperedges:
      (none)
  Member "generative adversarial networks recent developments":
    also connected through other hyperedges:
      (none)
  Member "towards photographic image manipulation with balanced growing of generative autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "sliced wasserstein autoencoder an embarrassingly simple generative model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "pioneer networks progressively growing generative autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "cramer wold autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards photographic image manipulation with balanced growing of generative autoencoders")
  Member "set aggregation network for structured data processing":
    also connected through other hyperedges:
      (none)
  Member "revisiting small batch training for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)