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 "a plug in method for representation factorization"):
  Abstract: In this work, we focus on decomposing the latent representations in GANs or learned feature representations in deep auto-encoders into semantically controllable factors in a semi-supervised manner, without modifying the original trained models. Specifically, we propose a Factors Decomposer-Entangler Network (FDEN) that learns to decompose a latent representation into mutually independent factors. Given a latent representation, the proposed framework draws a set of interpretable factors, each aligned to independent factors of variations by maximizing their total correlation in an information-theoretic means. As a plug-in method, we have applied our proposed FDEN to the existing networks of Adversarially Learned Inference and Pioneer Network and conducted computer vision tasks of image-to-image translation in semantic ways, e.g., changing styles while keeping an identify of a subject, and object classification in a few-shot learning scheme. We have also validated the effectiveness of our method with various ablation studies in qualitative, quantitative, and statistical examination.

Includes members:
  Member "seeing what a gan cannot generate":
    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 "wasserstein dependency measure for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised state representation learning in atari")
  Member "a unified feature disentangler for multi domain image translation and manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "generative adversarial networks gans what it can generate and what it cannot":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel framework for selection of gans for an application")
  Member "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "neural voice cloning with a few samples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speech2phone a multilingual and text independent speaker identification model")
  Member "learning deep disentangled embeddings with the f statistic loss":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)