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: "supervised matrix factorization for cross modality hashing"
  Abstract: Matrix factorization has been recently utilized for the task of multi-modal hashing for cross-modality visual search, where basis functions are learned to map data from different modalities to the same Hamming embedding. In this paper, we propose a novel cross-modality hashing algorithm termed Supervised Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing problem with a collective non-matrix factorization across the different modalities. In particular, SMFH employs a well-designed binary code learning algorithm to preserve the similarities among multi-modal original features through a graph regularization. At the same time, semantic labels, when available, are incorporated into the learning procedure. We conjecture that all these would facilitate to preserve the most relevant information during the binary quantization process, and hence improve the retrieval accuracy. We demonstrate the superior performance of SMFH on three cross-modality visual search benchmarks, i.e., the PASCAL-Sentence, Wiki, and NUS-WIDE, with quantitative comparison to various state-of-the-art methods

Connected through hyperedge HE-1 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multi view features"):
  with members:
    - "learning shared semantic space with correlation alignment for cross modal event retrieval"
    - "semi supervised hashing for semi paired cross view retrieval"
    - "discriminative supervised hashing for cross modal similarity search"
    - "hashing for similarity search a survey"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "unsupervised concatenation hashing with sparse constraint for cross modal retrieval"):
  with members:
    - "semi supervised hashing for semi paired cross view retrieval"
    - "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features"
    - "cross domain visual matching via generalized similarity measure and feature learning"

Connected through hyperedge HE-3 (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"
    - "deep cross modal hashing"
    - "transductive multi view zero shot learning"
    - "hashing on nonlinear manifolds"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-4 (induced by source paper "modeling text with graph convolutional network for cross modal information retrieval"):
  with members:
    - "distance metric learning using graph convolutional networks application to functional brain networks"
    - "learning deep structure preserving image text embeddings"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "very deep convolutional networks for large scale image recognition"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "discriminative supervised hashing for cross modal similarity search"):
  with members:
    - "semi supervised hashing for semi paired cross view retrieval"
    - "learning discriminative hashing codes for cross modal retrieval based on multiorder statistical features"
    - "ordinal constrained binary code learning for nearest neighbor search"

Connected through hyperedge HE-6 (induced by source paper "an overview of cross media retrieval concepts methodologies benchmarks and challenges"):
  with members:
    - "learning what and where to draw"
    - "generative adversarial text to image synthesis"
    - "learning aligned cross modal representations from weakly aligned data"
    - "learning to hash for indexing big data a survey"
    - "modality dependent cross media retrieval"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "show and tell a neural image caption generator"
    - "a multi view embedding space for modeling internet images tags and their semantics"

Connected through hyperedge HE-7 (induced by source paper "discrete latent factor model for cross modal hashing"):
  with members:
    - "hashing with binary autoencoders"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"

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.IR(Information Retrieval)