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: "discrete hashing with deep neural network"
  Abstract: This paper addresses the problem of learning binary hash codes for large scale image search by proposing a novel hashing method based on deep neural network. The advantage of our deep model over previous deep model used in hashing is that our model contains necessary criteria for producing good codes such as similarity preserving, balance and independence. Another advantage of our method is that instead of relaxing the binary constraint of codes during the learning process as most previous works, in this paper, by introducing the auxiliary variable, we reformulate the optimization into two sub-optimization steps allowing us to efficiently solve binary constraints without any relaxation. #R##N#The proposed method is also extended to the supervised hashing by leveraging the label information such that the learned binary codes preserve the pairwise label of inputs. #R##N#The experimental results on three benchmark datasets show the proposed methods outperform state-of-the-art hashing methods.

Connected through hyperedge HE-1 (induced by source paper "deep binary reconstruction for cross modal hashing"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "a comprehensive survey on cross modal retrieval"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "compact hash codes for efficient visual descriptors retrieval in large scale databases"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "supervised hashing with deep neural networks"
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "cnn based hashing for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "packing and padding coupled multi index for accurate image retrieval"

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)