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: "semi supervised hashing for semi paired cross view retrieval"
  Abstract: Recently, hashing techniques have gained importance in large-scale retrieval tasks because of their retrieval speed. Most of the existing cross-view frameworks assume that data are well paired. However, the fully-paired multiview situation is not universal in real applications. The aim of the method proposed in this paper is to learn the hashing function for semi-paired cross-view retrieval tasks. To utilize the label information of partial data, we propose a semi-supervised hashing learning framework which jointly performs feature extraction and classifier learning. The experimental results on two datasets show that our method outperforms several state-of-the-art methods in terms of retrieval accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multi view features"):
  with members:
    - "learning shared semantic space with correlation alignment for cross modal event retrieval"
    - "discriminative supervised hashing for cross modal similarity search"
    - "supervised matrix factorization for cross modality hashing"
    - "hashing for similarity search a survey"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "unsupervised concatenation hashing via combining subspace learning and graph embedding for cross modal image retrieval"):
  with members:
    - "learning discriminative hashing codes for cross modal retrieval based on multi view features"
    - "cross domain visual matching via generalized similarity measure and feature learning"

Connected through hyperedge HE-3 (induced by source paper "cross modal subspace learning with kernel correlation maximization and discriminative structure preserving"):
  with members:
    - "discriminative supervised hashing for cross modal similarity search"
    - "a multi view embedding space for modeling internet images tags and their semantics"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "unsupervised concatenation hashing with sparse constraint for cross modal retrieval"):
  with members:
    - "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "supervised matrix factorization for cross modality hashing"

Connected through hyperedge HE-5 (induced by source paper "discriminative supervised hashing for cross modal similarity search"):
  with members:
    - "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features"
    - "ordinal constrained binary code learning for nearest neighbor search"
    - "supervised matrix factorization for cross modality hashing"

Connected through hyperedge HE-6 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features"):
  with members:
    - "twitter100k a real world dataset for weakly supervised cross media retrieval"
    - "hashing for similarity search a survey"

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)