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: "robust subspace clustering with compressed data"
  Abstract: Dimension reduction is widely regarded as an effective way for decreasing the computation, storage, and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called  compressive robust subspace clustering , which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method,  row space pursuit  (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.

Connected through hyperedge HE-1 (induced by source paper "convolutional dictionary pair learning network for image representation learning"):
  with members:
    - "majorization minimization technique for optimally solving deep dictionary learning"
    - "multilayer collaborative low rank coding network for robust deep subspace discovery"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "deep micro dictionary learning and coding network"

Connected through hyperedge HE-2 (induced by source paper "multi view subspace clustering via partition fusion"):
  with members:
    - "robust graph learning from noisy data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "multi view multiple clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "exploring graph learning for semi supervised classification beyond euclidean data"
    - "multiple partitions aligned clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "unified spectral clustering with optimal graph"

Connected through hyperedge HE-3 (induced by source paper "multilayer collaborative low rank coding network for robust deep subspace discovery"):
  with members:
    - "robust graph learning from noisy data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "robust triple matrix recovery based auto weighted label propagation for classification"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "adaptive structure constrained robust latent low rank coding for image recovery"
    - "subspace clustering by block diagonal representation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "deep self representative concept factorization network for representation learning"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "the trustworthy pal controlling the false discovery rate in boolean matrix factorization"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "did distributed incremental block coordinate descent for nonnegative matrix factorization"
    - "latitude a model for mixed linear tropical matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "discriminative local sparse representation by robust adaptive dictionary pair learning"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "learning structured twin incoherent twin projective latent dictionary pairs for classification"
    - "scalable block diagonal locality constrained projective dictionary learning"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "kernel induced label propagation by mapping for semi supervised classification"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "fast low rank shared dictionary learning for image classification"

Connected through hyperedge HE-6 (induced by source paper "latent complete row space recovery for multi view subspace clustering"):
  with members:
    - "multi view intact space learning"
    - "multi view low rank sparse subspace clustering"
    - "essential tensor learning for multi view spectral clustering"
    - "subspace clustering by block diagonal representation"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"

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)