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: "deep sparse subspace clustering"
  Abstract: In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC). Regularized by the unit sphere distribution assumption for the learned deep features, DSSC can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by neural networks. One of the appealing advantages brought by DSSC is: when original real-world data do not meet the class-specific linear subspace distribution assumption, DSSC can employ neural networks to make the assumption valid with its hierarchical nonlinear transformations. To the best of our knowledge, this is among the first deep learning based subspace clustering methods. Extensive experiments are conducted on four real-world datasets to show the proposed DSSC is significantly superior to 12 existing methods for subspace clustering.

Connected through hyperedge HE-1 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "adaptive low rank kernel subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "multi graph fusion for multi view spectral clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "clustering with similarity preserving"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-3 (induced by source paper "scalable deep k subspace clustering"):
  with members:
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "robust multi body feature tracker a segmentation free approach"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "analysis and optimization of convolutional neural network architectures"
    - "deep subspace clustering networks"
    - "distance based confidence score for neural network classifiers"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "deep unsupervised clustering using mixture of autoencoders"):
  with members:
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "infinite variational autoencoder for semi supervised learning"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"

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)