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 "multi level representation learning for deep subspace clustering"):
  Abstract: This paper proposes a novel deep subspace clustering approach which uses convolutional autoencoders to transform input images into new representations lying on a union of linear subspaces. The first contribution of our work is to insert multiple fully-connected linear layers between the encoder layers and their corresponding decoder layers to promote learning more favorable representations for subspace clustering. These connection layers facilitate the feature learning procedure by combining low-level and high-level information for generating multiple sets of self-expressive and informative representations at different levels of the encoder. Moreover, we introduce a novel loss minimization problem which leverages an initial clustering of the samples to effectively fuse the multi-level representations and recover the underlying subspaces more accurately. The loss function is then minimized through an iterative scheme which alternatively updates the network parameters and produces new clusterings of the samples. Experiments on four real-world datasets demonstrate that our approach exhibits superior performance compared to the state-of-the-art methods on most of the subspace clustering problems.

Includes members:
  Member "self supervised convolutional subspace clustering network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "deep spectral clustering using dual autoencoder network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving raven s progressive matrices with neural networks")
  Member "neural collaborative subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep closed form subspace clustering")
  Member "unsupervised image to image translation using domain specific variational information bound":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "clustergan latent space clustering in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised k modal styled content generation")
  Member "deep multimodal subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational inference for deep probabilistic canonical correlation analysis")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "deep subspace clustering networks":
    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.CV(Computer Vision and Pattern Recognition)