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 "word2visualvec image and video to sentence matching by visual feature prediction"):
  Abstract: This paper strives to find the sentence best describing the content of an image or video. Different from existing works, which rely on a joint subspace for image / video to sentence matching, we propose to do so in a visual space only. We contribute Word2VisualVec, a deep neural network architecture that learns to predict a deep visual encoding of textual input based on sentence vectorization and a multi-layer perceptron. We thoroughly analyze its architectural design, by varying the sentence vectorization strategy, network depth and the deep feature to predict for image to sentence matching. We also generalize Word2VisualVec for matching a video to a sentence, by extending the predictive abilities to 3-D ConvNet features as well as a visual-audio representation. Experiments on four challenging image and video benchmarks detail Word2VisualVec's properties, capabilities for image and video to sentence matching, and on all datasets its state-of-the-art results.

Includes members:
  Member "deep learning for video classification and captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "the imagenet shuffle reorganized pre training for video event detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "jointly modeling embedding and translation to bridge video and language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval")
  Member "eventnet a large scale structured concept library for complex event detection in video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "videograph recognizing minutes long human activities in videos")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)