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: "a data driven approach for tag refinement and localization in web videos"
  Abstract: Our approach locates the temporal positions of tags in videos at the keyframe level.We deal with a scenario in which there is no pre-defined set of tags.We report experiments about the use of different web sources (Flickr, Google, Bing).We show state-of-the-art results on DUT-WEBV, a large dataset of YouTube videos.We show results in a real-world scenario to perform open vocabulary tag annotation. Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g.?using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select "on the fly" from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "layout induced video representation for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "yfcc100m the new data in multimedia research"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "webly supervised learning of convolutional networks"
    - "unsupervised learning of video representations using lstms"
    - "automatic concept discovery from parallel text and visual corpora"

Connected through hyperedge HE-3 (induced by source paper "query adaptive video summarization via quality aware relevance estimation"):
  with members:
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video2gif automatic generation of animated gifs from video"
    - "tagbook a semantic video representation without supervision for event detection"
    - "objects2action classifying and localizing actions without any video example"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-4 (induced by source paper "recognizing and presenting the storytelling video structure with deep multimodal networks"):
  with members:
    - "videostory embeddings recognize events when examples are scarce"
    - "a deep siamese network for scene detection in broadcast videos"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "scene driven retrieval in edited videos using aesthetic and semantic deep features"):
  with members:
    - "a deep siamese network for scene detection in broadcast videos"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "knowledge transfer for scene specific motion prediction"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "forecasting social navigation in crowded complex scenes"
    - "learning to divide and conquer for online multi target tracking"
    - "anticipating visual representations from unlabeled video"
    - "joint inference of groups events and human roles in aerial videos"
    - "anticipating the future by watching unlabeled 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)