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 "discovery and separation of features for invariant representation learning"):
  Abstract: Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust neural networks that induces invariance to nuisances through learning to discover and separate predictive and nuisance factors of data. We present an information theoretic formulation of our approach, from which we derive training objectives and its connections with previous methods. Empirical results on a wide array of datasets show that the proposed framework achieves state-of-the-art performance, without requiring nuisance annotations during training.

Includes members:
  Member "unified adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "invariant representations through adversarial forgetting")
  Member "invariant representations through adversarial forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization")
  Member "exact rate distortion in autoencoders via echo noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a free energy principle for representation learning")
  Member "ropad robust presentation attack detection through unsupervised adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "use of a capsule network to detect fake images and videos")
  Member "niesr nuisance invariant end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "invariant representations through adversarial forgetting")
  Member "invariant representations without adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "unsupervised adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "information constraints on auto encoding variational bayes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")

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)