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: "learning joint representations of videos and sentences with web image search"
  Abstract: Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "fine grained action retrieval through multiple parts of speech embeddings"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "higher order network for action recognition"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "on the effectiveness of task granularity for transfer learning"
    - "cross modal embeddings for video and audio retrieval"

Connected through hyperedge HE-3 (induced by source paper "cross modal interaction networks for query based moment retrieval in videos"):
  with members:
    - "video re localization"
    - "multilevel language and vision integration for text to clip retrieval"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "dense captioning events in videos"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-4 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "connecting touch and vision via cross modal prediction"):
  with members:
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "toward multimodal image to image translation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-6 (induced by source paper "multimodal abstractive summarization for how2 videos"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "how2 a large scale dataset for multimodal language understanding"
    - "product title refinement via multi modal generative adversarial learning"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "get to the point summarization with pointer generator networks"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised video moment retrieval via semantic completion network"):
  with members:
    - "weakly supervised video moment retrieval from text queries"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "mass masked sequence to sequence pre training for language generation"
    - "language models with transformers"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-8 (induced by source paper "predicting visual features from text for image and video caption retrieval"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "generative adversarial text to image synthesis"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "image captioning with deep bidirectional lstms"
    - "rnn fisher vectors for action recognition and image annotation"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "eventnet a large scale structured concept library for complex event detection in video"

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)