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: "deep hashing a joint approach for image signature learning"
  Abstract: Similarity-based image hashing represents crucial technique for visual data storage reduction and expedited image search. Conventional hashing schemes typically feed hand-crafted features into hash functions, which separates the procedures of feature extraction and hash function learning. In this paper, we propose a novel algorithm that concurrently performs feature engineering and non-linear supervised hashing function learning. Our technical contributions in this paper are two-folds: 1) deep network optimization is often achieved by gradient propagation, which critically requires a smooth objective function. The discrete nature of hash codes makes them not amenable for gradient-based optimization. To address this issue, we propose an exponentiated hashing loss function and its bilinear smooth approximation. Effective gradient calculation and propagation are thereby enabled; 2) pre-training is an important trick in supervised deep learning. The impact of pre-training on the hash code quality has never been discussed in current deep hashing literature. We propose a pre-training scheme inspired by recent advance in deep network based image classification, and experimentally demonstrate its effectiveness. Comprehensive quantitative evaluations are conducted on several widely-used image benchmarks. On all benchmarks, our proposed deep hashing algorithm outperforms all state-of-the-art competitors by significant margins. In particular, our algorithm achieves a near-perfect 0.99 in terms of Hamming ranking accuracy with only 12 bits on MNIST, and a new record of 0.74 on the CIFAR10 dataset. In comparison, the best accuracies obtained on CIFAR10 by existing hashing algorithms without or with deep networks are known to be 0.36 and 0.58 respectively.

Connected through hyperedge HE-1 (induced by source paper "bilinear supervised hashing based on 2d image features"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "a survey on learning to hash"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "bilinear random projections for locality sensitive binary codes"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-2 (induced by source paper "learning beyond datasets knowledge graph augmented neural networks for natural language processing"):
  with members:
    - "machine learning with world knowledge the position and survey"
    - "lstm a search space odyssey"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "a survey of inductive biases for factorial representation learning"
    - "the more you know using knowledge graphs for image classification"
    - "proje embedding projection for knowledge graph completion"
    - "effective approaches to attention based neural machine translation"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-3 (induced by source paper "a revisit on deep hashings for large scale content based image retrieval"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"

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)