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: "eventnet a large scale structured concept library for complex event detection in video"
  Abstract: Event-specific concepts are the semantic concepts designed for the events of interest, which can be used as a mid-level representation of complex events in videos. Existing methods only focus on defining event-specific concepts for a small number of predefined events, but cannot handle novel unseen events. This motivates us to build a large scale event-specific concept library that covers as many real-world events and their concepts as possible. Specifically, we choose WikiHow, an online forum containing a large number of how-to articles on human daily life events. We perform a coarse-to-fine event discovery process and discover 500 events from WikiHow articles. Then we use each event name as query to search YouTube and discover event-specific concepts from the tags of returned videos. After an automatic filter process, we end up with 95,321 videos and 4,490 concepts. We train a Convolutional Neural Network (CNN) model on the 95,321 videos over the 500 events, and use the model to extract deep learning feature from video content. With the learned deep learning feature, we train 4,490 binary SVM classifiers as the event-specific concept library. The concepts and events are further organized in a hierarchical structure defined by WikiHow, and the resultant concept library is called EventNet. Finally, the EventNet concept library is used to generate concept based representation of event videos. To the best of our knowledge, EventNet represents the first video event ontology that organizes events and their concepts into a semantic structure. It offers great potential for event retrieval and browsing. Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207%.

Connected through hyperedge HE-1 (induced by source paper "videograph recognizing minutes long human activities in videos"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "video time properties encoders and evaluation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "video action transformer network"

Connected through hyperedge HE-2 (induced by source paper "timeception for complex action recognition"):
  with members:
    - "video time properties encoders and evaluation"
    - "what have we learned from deep representations for action recognition"
    - "videos as space time region graphs"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "learning joint representations of videos and sentences with web image search"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "soccernet a scalable dataset for action spotting in soccer videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "temporal action detection with structured segment networks"
    - "learnable pooling with context gating for video classification"
    - "temporal context network for activity localization in videos"
    - "action tubelet detector for spatio temporal action localization"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-5 (induced by source paper "exploiting feature and class relationships in video categorization with regularized deep neural networks"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "going deeper with convolutions"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "using web co occurrence statistics for improving image categorization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep learning for video classification and captioning"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "the thumos challenge on action recognition for videos in the wild"
    - "real time action recognition with enhanced motion vector cnns"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "videolstm convolves attends and flows for action recognition"

Connected through hyperedge HE-7 (induced by source paper "video generation from text"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "one step time dependent future video frame prediction with a convolutional encoder decoder neural network"
    - "forecasting human dynamics from static images"
    - "the kinetics human action video dataset"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "video imagination from a single image with transformation generation"
    - "transformation based models of video sequences"

Connected through hyperedge HE-8 (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"

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)