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 "direct optimization through arg max for discrete variational auto encoder"):
  Abstract: Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. Our work optimizes the discrete VAE objective directly, using its Gumbel-Max reparameterization, by applying the direct loss minimization technique to generative models. This optimization technique propagates gradients through the reparameterized $\arg \max$, which are estimated by the difference of gradients of two $\arg \max$ predictions. This realization provides the means to learn latent representations in cases when evaluating the $\arg \max$ operation is tractable while evaluating the softmax operation is intractable.

Includes members:
  Member "nash toward end to end neural architecture for generative semantic hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction")
  Member "structvae tree structured latent variable models for semi supervised semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a probabilistic formulation of unsupervised text style transfer")
  Member "dvae discrete variational autoencoders with overlapping transformations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pixelvae improved pixelvae with discrete prior")
  Member "toward controlled generation of text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust explanations for visual question answering")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "rebar low variance unbiased gradient estimates for discrete latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "learning hard alignments with variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "select and attend towards controllable content selection in text generation")

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)