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 "invariant representations through adversarial forgetting"):
  Abstract: We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves state-of-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.

Includes members:
  Member "unified adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovery and separation of features for invariant representation learning")
  Member "ropad robust presentation attack detection through unsupervised adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "discovery and separation of features for invariant representation learning")
  Member "invariant representations without adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "unsupervised adversarial invariance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "information constraints on auto encoding variational bayes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "emergence of invariance and disentanglement in deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network")

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)