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: "hashing on nonlinear manifolds"
  Abstract: Learning-based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes preserving the Euclidean similarity in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexities of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this paper, how to learn compact binary embeddings on their intrinsic manifolds is considered. In order to address the above-mentioned difficulties, an efficient, inductive solution to the out-of-sample data problem, and a process by which nonparametric manifold learning may be used as the basis of a hashing method are proposed. The proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. It is particularly shown that hashing on the basis of t-distributed stochastic neighbor embedding outperforms state-of-the-art hashing methods on large-scale benchmark data sets, and is very effective for image classification with very short code lengths. It is shown that the proposed framework can be further improved, for example, by minimizing the quantization error with learned orthogonal rotations without much computation overhead. In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.

Connected through hyperedge HE-1 (induced by source paper "deep multi view enhancement hashing for image retrieval"):
  with members:
    - "deep multi view learning using neuron wise correlation maximizing regularizers"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "deep cross modal hashing"
    - "using complex networks towards information retrieval and diagnostics in multidimensional imaging"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "ssdh semi supervised deep hashing for large scale image retrieval"):
  with members:
    - "a survey on learning to hash"
    - "how should we evaluate supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "video segment copy detection using memory constrained hierarchical batch normalized lstm autoencoder"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "recurrent batch normalization"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "optimized cartesian k means"
    - "hashing for similarity search a survey"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-5 (induced by source paper "query adaptive hash code ranking for large scale multi view visual search"):
  with members:
    - "multi view intact space learning"
    - "projection bank from high dimensional data to medium length binary codes"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-6 (induced by source paper "graph based multi view binary learning for image clustering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "highly economized multi view binary compression for scalable image clustering"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "global hashing system for fast image search"):
  with members:
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "inductive hashing on manifolds"
    - "compact hyperplane hashing with bilinear functions"

Connected through hyperedge HE-8 (induced by source paper "attribute guided network for cross modal zero shot hashing"):
  with members:
    - "transductive zero shot hashing via coarse to fine similarity mining"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "hashnet deep learning to hash by continuation"
    - "transductive zero shot learning with adaptive structural embedding"
    - "supervised matrix factorization for cross modality hashing"
    - "deep cross modal hashing"
    - "transductive multi view zero shot learning"
    - "learning to hash for indexing big data 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)