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 "unsupervised concatenation hashing via combining subspace learning and graph embedding for cross modal image retrieval"):
  Abstract: Different from the content-based image retrieval methods, cross-modal image retrieval methods uncover the rich semantic-level information of social images to further understand image contents. As multiple modal data depict a common object from multiple perspectives, many works focus on learning the unified subspace representation. Recently, hash representation has received much attention in the retrieval field. In common Hamming space, how to directly preserve the local manifold structure among objects become an interesting problem. Most of the unsupervised hashing methods attempt to solve it by constructing a neighborhood graph on every modality respectively. However, it is hard to decide the weight factor of each graph to get the optimal graph. To overcome this problem, we adopt the concatenated features to represent the common object since the information implied by different modalities is complementary. In our framework, Locally Linear Embedding and Locality Preserving Projection are introduced to reconstruct the manifold structure of the original space. Besides, The $\ell_{2,1}$-norm constraint is imposed on the projection matrices to explore the discriminative hashing functions. Extensive experiments are performed on three public datasets and the experimental results show that our method outperforms several classic unsupervised hashing models.

Includes members:
  Member "learning discriminative hashing codes for cross modal retrieval based on multi view features":
    also connected through other hyperedges:
      (none)
  Member "semi supervised hashing for semi paired cross view retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multi view features")
  Member "cross domain visual matching via generalized similarity measure and feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark")

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)