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: "to click or not to click automatic selection of beautiful thumbnails from videos"
  Abstract: Thumbnails play such an important role in online videos. As the most representative snapshot, they capture the essence of a video and provide the first impression to the viewers; ultimately, a great thumbnail makes a video more attractive to click and watch. We present an automatic thumbnail selection system that exploits two important characteristics commonly associated with meaningful and attractive thumbnails: high relevance to video content and superior visual aesthetic quality. Our system selects attractive thumbnails by analyzing various visual quality and aesthetic metrics of video frames, and performs a clustering analysis to determine the relevance to video content, thus making the resulting thumbnails more representative of the video. On the task of predicting thumbnails chosen by professional video editors, we demonstrate the effectiveness of our system against six baseline methods, using a real-world dataset of 1,118 videos collected from Yahoo Screen. In addition, we study what makes a frame a good thumbnail by analyzing the statistical relationship between thumbnail frames and non-thumbnail frames in terms of various image quality features. Our study suggests that the selection of a good thumbnail is highly correlated with objective visual quality metrics, such as the frame texture and sharpness, implying the possibility of building an automatic thumbnail selection system based on visual aesthetics.

Connected through hyperedge HE-1 (induced by source paper "image to video domain adaptation using web supervision"):
  with members:
    - "importance weighted adversarial nets for partial domain adaptation"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "sentence specified dynamic video thumbnail generation"):
  with members:
    - "weakly supervised spatio temporally grounding natural sentence in video"
    - "videos as space time region graphs"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "attention is all you need"
    - "dense captioning events in videos"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-3 (induced by source paper "less is more picking informative frames for video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "image captioning with semantic attention"
    - "hierarchical co attention for visual question answering"
    - "frame and segment level features and candidate pool evaluation for video caption generation"

Connected through hyperedge HE-4 (induced by source paper "fast video shot transition localization with deep structured models"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "from thumbnails to summaries a single deep neural network to rule them all"):
  with members:
    - "energy based generative adversarial network"
    - "video summarization with long short term memory"
    - "rethinking the inception architecture for computer vision"
    - "video language modeling a baseline for generative models of natural videos"

Connected through hyperedge HE-6 (induced by source paper "real time video summarization on commodity hardware"):
  with members:
    - "video summarization with long short term memory"
    - "deep residual learning for image recognition"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "the beauty of capturing faces rating the quality of digital portraits"

Connected through hyperedge HE-7 (induced by source paper "query adaptive video summarization via quality aware relevance estimation"):
  with members:
    - "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"
    - "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-8 (induced by source paper "beautiful and damned combined effect of content quality and social ties on user engagement"):
  with members:
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "the majority illusion in social networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "yfcc100m the new data in multimedia research"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "the beauty of capturing faces rating the quality of digital portraits"
    - "imagenet large scale visual recognition challenge"
    - "6 seconds of sound and vision creativity in micro 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.MM(Multimedia)