human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "scalable deep k subspace clustering"
  Abstract: Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.

Connected through hyperedge HE-1 (induced by source paper "neural collaborative subspace clustering"):
  with members:
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep subspace clustering networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adaptive low rank kernel subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "deep closed form subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "embarrassingly shallow autoencoders for sparse data"
    - "neural collaborative subspace clustering"
    - "deep subspace clustering networks"
    - "robust multi body feature tracker a segmentation free approach"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Question: Please tell me which class the center node 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)