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: "highly economized multi view binary compression for scalable image clustering"
  Abstract: How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under L21-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.

Connected through hyperedge HE-1 (induced by source paper "sadih semantic aware discrete hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "supervised discrete hashing with relaxation"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "very deep convolutional networks for large scale image recognition"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-2 (induced by source paper "graph based multi view binary learning for image clustering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "hashing on nonlinear manifolds"

Connected through hyperedge HE-3 (induced by source paper "collaborative learning for extremely low bit asymmetric hashing"):
  with members:
    - "from hashing to cnns training binaryweight networks via hashing"
    - "deep mutual learning"
    - "asymmetric deep supervised hashing"
    - "part based deep hashing for large scale person re identification"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "incomplete multi view clustering via graph regularized matrix factorization"):
  with members:
    - "discriminative block diagonal representation learning for image recognition"
    - "robust recovery of subspace structures by low rank representation"

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)