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: "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.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-2 (induced by source paper "use what you have video retrieval using representations from collaborative experts"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "cross task weakly supervised learning from instructional videos"
    - "on the variance of the adaptive learning rate and beyond"
    - "videobert a joint model for video and language representation learning"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "massively parallel hyperparameter tuning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised video moment retrieval from text queries"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "multilevel language and vision integration for text to clip retrieval"
    - "webly supervised joint embedding for cross modal image text retrieval"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-4 (induced by source paper "action2vec a crossmodal embedding approach to action learning"):
  with members:
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "convolutional two stream network fusion for video action recognition"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "learning attributes equals multi source domain generalization"

Connected through hyperedge HE-5 (induced by source paper "interactive video retrieval with dialog"):
  with members:
    - "audio visual scene aware dialog"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "the kinetics human action video dataset"
    - "a comprehensive survey on cross modal retrieval"

Connected through hyperedge HE-6 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval"):
  with members:
    - "exploring stereotypes and biased data with the crowd"
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "dual path convolutional image text embedding with instance loss"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning social image embedding with deep multimodal attention networks"

Connected through hyperedge HE-7 (induced by source paper "activity recognition on a large scale in short videos moments in time dataset"):
  with members:
    - "a closer look at weak label learning for audio events"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "temporal relational reasoning in videos"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "cuhk ethz siat submission to activitynet challenge 2016"
    - "review of action recognition and detection methods"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-8 (induced by source paper "a visual distance for wordnet"):
  with members:
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image 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)