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: "inductive hashing on 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 that preserve the Euclidean distance 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 complexity 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 work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our 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. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.

Connected through hyperedge HE-1 (induced by source paper "proximity preserving binary code using signed graph cut"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "efficient estimation of word representations in vector space"
    - "the slashdot zoo mining a social network with negative edges"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot hashing for multi label image retrieval"):
  with members:
    - "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"
    - "supervised discrete hashing with relaxation"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "hashing in the zero shot framework with domain adaptation"
    - "hashnet deep learning to hash by continuation"
    - "scalable image retrieval by sparse product quantization"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"

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"
    - "hashing on nonlinear manifolds"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "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"
    - "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 "fast supervised discrete hashing"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "supervised discrete hashing with relaxation"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "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"
    - "sparse principal component analysis via rotation and truncation"

Connected through hyperedge HE-7 (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"
    - "hashing on nonlinear manifolds"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"

Connected through hyperedge HE-8 (induced by source paper "extracting visual knowledge from the internet making sense of image data"):
  with members:
    - "towards automatic construction of diverse high quality image dataset"
    - "exploiting web images for dataset construction a domain robust approach"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "the google similarity distance"

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.LG(Machine Learning)