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 "mine mutual information neural estimation"):
  Abstract: This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.

Includes members:
  Member "dual discriminator generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination")
  Member "multi agent diverse generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a mean field analysis of two player zero sum games")
  Member "it takes only two adversarial generator encoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lia latently invertible autoencoder with adversarial learning")
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial code learning for image generation")
  Member "semi supervised learning with gans manifold invariance with improved inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end graph based deep semi supervised learning")
  Member "pacgan the power of two samples in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "calibrating energy based generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "ensemble estimation of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mint deep network compression via mutual information based neuron trimming")

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)