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: "tagbook a semantic video representation without supervision for event detection"
  Abstract: We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.

Connected through hyperedge HE-1 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "fast zero shot image tagging"
    - "person re identification by discriminative selection in video ranking"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "long activity video understanding using functional object oriented network"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "prediction of manipulation actions"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "learning from multiple sources for video summarisation"
    - "an end to end generative framework for video segmentation and recognition"
    - "p cnn pose based cnn features for action recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "unified embedding and metric learning for zero exemplar event detection"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "rank pooling for action recognition"
    - "dynamic concept composition for zero example event detection"
    - "video stream retrieval of unseen queries using semantic memory"
    - "places an image database for deep scene understanding"
    - "audio event detection using weakly labeled data"
    - "jointly modeling embedding and translation to bridge video and language"
    - "zero shot event detection by multimodal distributional semantic embedding of videos"

Connected through hyperedge HE-4 (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"
    - "a data driven approach for tag refinement and localization in web videos"
    - "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-5 (induced by source paper "vrfp on the fly video retrieval using web images and fast fisher vector products"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "sift meets cnn a decade survey of instance retrieval"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "objects2action classifying and localizing actions without any video example"
    - "temporal localization of fine grained actions in videos by domain transfer from web images"
    - "building a large concept bank for representing events in video"
    - "caffe convolutional architecture for fast feature embedding"

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)