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: "semantic cluster unary loss for efficient deep hashing"
  Abstract: With the rapid development of deep learning, deep hashing methods have achieved promising results in efficient information retrieval. The hashing method maps similar data to binary hashcodes with smaller hamming distance, which has received broad attention due to its low storage cost and fast retrieval speed. Most of the existing deep hashing methods adopt pairwise or triplet losses to deal with similarities underlying the data, but their training is difficult and less efficient because    $O(n^{2})$    data pairs and    $O(n^{3})$    triplets are involved. To address these issues, we propose a novel deep hashing algorithm with the unary loss which can be trained very efficiently. First, we introduce a Unary Upper Bound of the traditional triplet loss, thus reducing the complexity to    $O(n)$    and bridging the classification-based unary loss and the triplet loss. Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by introducing a modified Unary Upper Bound loss, called Semantic Cluster Unary Loss. The resultant hashcodes form several compact clusters, which means the hashcodes in the same cluster have similar semantic information. We also demonstrate that the proposed SCDH is easy to extend to semi-supervised settings by incorporating the state-of-the-art semi-supervised learning algorithms. The experiments on large-scale datasets show that the proposed method is superior to the state-of-the-art hashing algorithms.

Connected through hyperedge HE-1 (induced by source paper "joint cluster unary loss for efficient cross modal hashing"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "fast training of triplet based deep binary embedding networks"
    - "correlation hashing network for efficient cross modal retrieval"
    - "deep cross modal hashing"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

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)