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: "temporal tessellation a unified approach for video analysis"
  Abstract: We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics -- natural language captions or other labels -- depends on the task at hand. A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video. We describe two matching methods, both designed to ensure that (a) reference clips appear similar to test clips and (b), taken together, the semantics of the selected reference clips is consistent and maintains temporal coherence. We use our method for video captioning on the LSMDC'16 benchmark, video summarization on the SumMe and TVSum benchmarks, Temporal Action Detection on the Thumos2014 benchmark, and sound prediction on the Greatest Hits benchmark. Our method not only surpasses the state of the art, in four out of five benchmarks, but importantly, it is the only single method we know of that was successfully applied to such a diverse range of tasks.

Connected through hyperedge HE-1 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"):
  with members:
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "look listen and learn"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "learning to learn from noisy web videos"

Connected through hyperedge HE-3 (induced by source paper "balancing specialization generalization and compression for detection and tracking"):
  with members:
    - "deep pnml predictive normalized maximum likelihood for deep neural networks"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "crest convolutional residual learning for visual tracking"
    - "mask r cnn"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "temporal sequence distillation towards few frame action recognition in videos"):
  with members:
    - "boosting up scene text detectors with guided cnn"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "appearance and relation networks for video classification"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"

Connected through hyperedge HE-5 (induced by source paper "on the effectiveness of task granularity for transfer learning"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "the something something video database for learning and evaluating visual common sense"
    - "generating captions without looking beyond objects"
    - "oracle performance for visual captioning"

Connected through hyperedge HE-6 (induced by source paper "hierarchical video understanding"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the something something video database for learning and evaluating visual common sense"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "excitation backprop for rnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "lstm a search space odyssey"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "end to end concept word detection for video captioning retrieval and question answering"):
  with members:
    - "image captioning with semantic attention"
    - "learning language visual embedding for movie understanding with natural language"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "video fill in the blank with merging lstms"
    - "captioning images with diverse objects"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "what value do explicit high level concepts have in vision to language problems"
    - "the long short story of movie description"

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)