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: "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
  Abstract: Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.

Connected through hyperedge HE-1 (induced by source paper "hyperlearn a distributed approach for representation learning in datasets with many modalities"):
  with members:
    - "social anchor unit graph regularized tensor completion for large scale image retagging"
    - "videos as space time region graphs"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "omniart multi task deep learning for artistic data analysis"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "using word embeddings for automatic query expansion"
    - "query expansion with locally trained word embeddings"
    - "query expansion via structural motifs in wikipedia graph"
    - "query expansion based on crowd knowledge for code search"
    - "exploring query categorisation for query expansion a study"
    - "query expansion using term distribution and term association"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-4 (induced by source paper "multi label zero shot learning with transfer aware label embedding projection"):
  with members:
    - "multi label zero shot learning with structured knowledge graphs"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "fast zero shot image tagging"
    - "cnn rnn a unified framework for multi label image classification"
    - "improving multi label learning with missing labels by structured semantic correlations"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-5 (induced by source paper "visir visual and semantic image label refinement"):
  with members:
    - "know2look commonsense knowledge for visual search"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "yolo9000 better faster stronger"
    - "cnn rnn a unified framework for multi label image classification"
    - "contextual action recognition with r cnn"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "lsda large scale detection through adaptation"

Connected through hyperedge HE-6 (induced by source paper "social anchor unit graph regularized tensor completion for large scale image retagging"):
  with members:
    - "image tag completion by low rank factorization with dual reconstruction structure preserved"
    - "fast exact search in hamming space with multi index hashing"
    - "matrix completion with noise"

Connected through hyperedge HE-7 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "yfcc100m the new data in multimedia research"
    - "webly supervised learning of convolutional networks"
    - "a data driven approach for tag refinement and localization in web videos"
    - "unsupervised learning of video representations using lstms"
    - "automatic concept discovery from parallel text and visual corpora"

Connected through hyperedge HE-8 (induced by source paper "automatic image annotation via label transfer in the semantic space"):
  with members:
    - "multi view intact space learning"
    - "multi instance visual semantic embedding"
    - "learning structured inference neural networks with label relations"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for 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.IR(Information Retrieval)