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 discriminative latent space for clustering"):
  Abstract: Clustering is one of the most fundamental tasks in data analysis and machine learning. It is central to many data-driven applications that aim to separate the data into groups with similar patterns. Moreover, clustering is a complex procedure that is affected significantly by the choice of the data representation method. Recent research has demonstrated encouraging clustering results by learning effectively these representations. In most of these works a deep auto-encoder is initially pre-trained to minimize a reconstruction loss, and then jointly optimized with clustering centroids in order to improve the clustering objective. Those works focus mainly on the clustering phase of the procedure, while not utilizing the potential benefit out of the initial phase. In this paper we propose to optimize an auto-encoder with respect to a discriminative pairwise loss function during the auto-encoder pre-training phase. We demonstrate the high accuracy obtained by the proposed method as well as its rapid convergence (e.g. reaching above 92% accuracy on MNIST during the pre-training phase, in less than 50 epochs), even with small networks.

Includes members:
  Member "deep continuous clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep clustering with a dynamic autoencoder from reconstruction towards centroids construction")
  Member "deep subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")
  Member "discriminatively boosted image clustering with fully convolutional auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep amortized clustering")
  Member "real time single image and video super resolution using an efficient sub pixel convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "variational deep embedding an unsupervised and generative approach to clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "towards k means friendly spaces simultaneous deep learning and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics")
  Member "joint unsupervised learning of deep representations and image clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")

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)