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 with sparse constraint for cross modal retrieval"):
  Abstract: With the advantage of low storage cost and high efficiency, hashing learning has received much attention in retrieval field. As multiple modal data representing a common object semantically are complementary, many works focus on learning unified binary codes. However, these works ignore the importance of manifold structre among data. In fact, it is still an interesting problem to directly preserve the local manifold structure among samples in hamming space. Since different modalities are isomerous, we adopt the concatenated feature of multiple modality feature to represent original object. In our framework, Locally Linear Embedding and Locality Preserving Projection are introduced to reconstruct the manifold structure of original space in the Hamming space. Besides, The L21-norm regularization are imposed on the projection matrices to further exploit the discriminative features for different modalities simultaneously. Extensive experiments are performed to evaluate the proposed method, dubbed Unsupervised Concatenation Hashing (UCH), on the three publicly available datasets and the experimental results show the superior performance of UCH outperforming most of state-of-the-art unsupervised hashing models.

Includes members:
  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 "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative supervised hashing for cross modal similarity search")
  Member "cross domain visual matching via generalized similarity measure and feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised concatenation hashing via combining subspace learning and graph embedding for cross modal image retrieval")
  Member "supervised matrix factorization for cross modality hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multi view features")

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)