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 "inverse learning of symmetry transformations"):
  Abstract: Symmetry transformations induce invariances and are a crucial building block of modern machine learning algorithms. Some transformations can be described analytically, e.g. geometric invariances. However, in many complex domains, such as the chemical space, invariances can be observed yet the corresponding symmetry transformation cannot be formulated analytically. Thus, the goal of our work is to learn the symmetry transformation that induced this invariance. To address this task, we propose learning two latent subspaces, where the first subspace captures the property and the second subspace the remaining invariant information. Our approach is based on the deep information bottleneck principle in combination with a mutual information regulariser. Unlike previous methods however, we focus on estimating mutual information in continuous rather than binary settings. This poses many challenges as mutual information cannot be meaningfully minimised in continuous domains. Therefore, we base the calculation of mutual information on correlation matrices in combination with a bijective variable transformation. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets.

Includes members:
  Member "learning extremal representations with deep archetypal analysis":
    also connected through other hyperedges:
      (none)
  Member "deep archetypal analysis":
    also connected through other hyperedges:
      (none)
  Member "on the difference between the information bottleneck and the deep information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  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 "invariant representations without adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "adversarial information factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local facial attribute transfer through inpainting")
  Member "automatic chemical design using a data driven continuous representation of molecules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "learning latent subspaces in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)