human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting visual features from text for image and video caption retrieval"):
  Abstract: This paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute  Word2VisualVec  , a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multiscale sentence vectorization and further transferred into a deep visual feature of choice via a simple multilayer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both three-dimensional convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset, and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition, and its state-of-the-art results.

Includes members:
  Member "end to end concept word detection for video captioning retrieval and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips")
  Member "generative adversarial text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "the imagenet shuffle reorganized pre training for video event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "icap interative image captioning with predictive text")
  Member "learning joint representations of videos and sentences with web image search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "use what you have video retrieval using representations from collaborative experts")
  Member "image captioning with deep bidirectional lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "rnn fisher vectors for action recognition and image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "learning deep structure preserving image text embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "multimodal convolutional neural networks for matching image and sentence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval")

Question: Please tell me which class the source paper 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)