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 "deep k means jointly clustering with k means and learning representations"):
  Abstract: We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for $k$-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.

Includes members:
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memory based graph networks")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "cnn based joint clustering and representation learning with feature drift compensation for large scale image data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "soft to hard vector quantization for end to end learning compressible representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to structure an image with few colors")
  Member "deep subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")
  Member "the concrete distribution a continuous relaxation of discrete random variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "variational deep embedding an unsupervised and generative approach to clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")

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)