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 "automatic differentiation variational inference with mixtures"):
  Abstract: Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned by ADVI are forced to be unimodal in order to facilitate use of the reparameterization trick. In this paper, we show how stratified sampling may be used to enable mixture distributions as the approximate posterior, and derive a new lower bound on the evidence analogous to the importance weighted autoencoder (IWAE). We show that this ``SIWAE'' is a tighter bound than both IWAE and the traditional ELBO, both of which are special instances of this bound. We verify empirically that the traditional ELBO objective disfavors the presence of multimodal posterior distributions and may therefore not be able to fully capture structure in the latent space. Our experiments show that using the SIWAE objective allows the encoder to learn more complex distributions which regularly contain multimodality, resulting in higher accuracy and better calibration in the presence of incomplete, limited, or corrupted data.

Includes members:
  Member "divide and couple using monte carlo variational objectives for posterior approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy inspired models learning with sampler induced distributions")
  Member "implicit reparameterization gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks on probabilistic autoregressive forecasting models")
  Member "modeling uncertainty with hedged instance embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "vae with a vampprior":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "stochastic backpropagation through mixture density distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial attacks on probabilistic autoregressive forecasting models")
  Member "deep variational information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine 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)