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: "transitive hashing network for heterogeneous multimedia retrieval"
  Abstract: Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency. Cross-modal hashing enables efficient retrieval from database of one modality in response to a query of another modality. Existing work on cross-modal hashing assumes heterogeneous relationship across modalities for hash function learning. In this paper, we relax the strong assumption by only requiring such heterogeneous relationship in an auxiliary dataset different from the query/database domain. We craft a hybrid deep architecture to simultaneously learn the cross-modal correlation from the auxiliary dataset, and align the dataset distributions between the auxiliary dataset and the query/database domain, which generates transitive hash codes for heterogeneous multimedia retrieval. Extensive experiments exhibit that the proposed approach yields state of the art multimedia retrieval performance on public datasets, i.e. NUS-WIDE, ImageNet-YahooQA.

Connected through hyperedge HE-1 (induced by source paper "feature level fusion from facial attributes for face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "label propagation for deep semi supervised learning"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "face attribute prediction using off the shelf cnn features"
    - "restoring highly corrupted images by impulse noise using radial basis functions interpolation"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "performance effectiveness of multimedia information search using the epsilon greedy algorithm"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "term relevance feedback for contextual named entity retrieval"
    - "darts differentiable architecture search"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "hierarchical representations for efficient architecture search"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "relevance based word embedding"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-3 (induced by source paper "discriminative cross view binary representation learning"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "a structured self attentive sentence embedding"
    - "end to end binary representation learning via direct binary embedding"
    - "fast training of triplet based deep binary embedding networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep cross modal hashing"
    - "bag of tricks for efficient text classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep cross media knowledge transfer"):
  with members:
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "cross modal common representation learning by hybrid transfer network"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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"
    - "deep hashing a joint approach for image signature learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "mhtn modal adversarial hybrid transfer network for cross modal retrieval"):
  with members:
    - "deep cross media knowledge transfer"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "cross modal common representation learning by hybrid transfer network"
    - "learning aligned cross modal representations from weakly aligned data"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "cross modal distillation for supervision transfer"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "transfer adversarial hashing for hamming space retrieval"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "hashing for similarity search a survey"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)