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: "video re localization"
  Abstract: Many methods have been developed to help people find the video contents they want efficiently. However, there are still some unsolved problems in this area. For example, given a query video and a reference video, how to accurately localize a segment in the reference video such that the segment semantically corresponds to the query video? We define a distinctively new task, namely \textbf{video re-localization}, to address this scenario. Video re-localization is an important emerging technology implicating many applications, such as fast seeking in videos, video copy detection, video surveillance, etc. Meanwhile, it is also a challenging research task because the visual appearance of a semantic concept in videos can have large variations. The first hurdle to clear for the video re-localization task is the lack of existing datasets. It is labor expensive to collect pairs of videos with semantic coherence or correspondence and label the corresponding segments. We first exploit and reorganize the videos in ActivityNet to form a new dataset for video re-localization research, which consists of about 10,000 videos of diverse visual appearances associated with localized boundary information. Subsequently, we propose an innovative cross gated bilinear matching model such that every time-step in the reference video is matched against the attentively weighted query video. Consequently, the prediction of the starting and ending time is formulated as a classification problem based on the matching results. Extensive experimental results show that the proposed method outperforms the competing methods. Our code is available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "a better baseline for ava"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-2 (induced by source paper "cross modal interaction networks for query based moment retrieval in videos"):
  with members:
    - "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"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "semantic conditioned dynamic modulation for temporal sentence grounding in videos"):
  with members:
    - "sentence specified dynamic video thumbnail generation"
    - "spatio temporal video re localization by warp lstm"
    - "weakly supervised spatio temporally grounding natural sentence in video"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "multi granularity generator for temporal action proposal"
    - "multilevel language and vision integration for text to clip retrieval"
    - "actor and action video segmentation from a sentence"

Connected through hyperedge HE-4 (induced by source paper "hallucinating optical flow features for video classification"):
  with members:
    - "spatio temporal video re localization by warp lstm"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "non local netvlad encoding for video classification"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-5 (induced by source paper "localizing unseen activities in video via image query"):
  with members:
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "single shot temporal action detection"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-6 (induced by source paper "visil fine grained spatio temporal video similarity learning"):
  with members:
    - "fivr fine grained incident video retrieval"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep multi task representation learning a tensor factorisation approach"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "non local netvlad encoding for video classification"):
  with members:
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "fine grained video attractiveness prediction using multimodal deep learning on a large real world dataset"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)