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 "sampling good latent variables via cpp vaes vaes with condition posterior as prior"):
  Abstract: In practice, conditional variational autoencoders (CVAEs) perform conditioning by combining two sources of information which are computed completely independently; CVAEs first compute the condition, then sample the latent variable, and finally concatenate these two sources of information. However, these two processes should be tied together such that the model samples a latent variable given the conditioning signal. In this paper, we directly address this by conditioning the sampling of the latent variable on the CVAE condition, thus encouraging it to carry relevant information. We study this specifically for tasks that leverage with strong conditioning signals and where the generative models have highly expressive decoders able to generate a sample based on the information contained in the condition solely. In particular, we experiments with the two challenging tasks of diverse human motion generation and diverse image captioning, for which our results suggest that unifying latent variable sampling and conditioning not only yields samples of higher quality, but also helps the model to avoid the posterior collapse, a known problem of VAEs with expressive decoders.

Includes members:
  Member "a surprisingly effective fix for deep latent variable modeling of text":
    also connected through other hyperedges:
      (none)
  Member "implicit deep latent variable models for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sentencemim a latent variable language model")
  Member "learning variations in human motion via mix and match perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "mixture content selection for diverse sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how decoding strategies affect the verifiability of generated text")
  Member "diverse trajectory forecasting with determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "learning trajectory dependencies for human motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "mixture models for diverse machine translation tricks of the trade":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the discrepancy between density estimation and sequence generation")
  Member "diversity sensitive conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blur noise and compression robust generative adversarial networks")

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)