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: "deep learning for video classification and captioning"
  Abstract: Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning"):
  with members:
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "on scale out deep learning training for cloud and hpc"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "a read write memory network for movie story understanding"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "end to end video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "to create what you tell generating videos from captions"
    - "reconstruction network for video captioning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "yotube searching action proposal via recurrent and static regression networks"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "object detection from video tubelets with convolutional neural networks"
    - "ssd single shot multibox detector"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "survey on the attention based rnn model and its applications in computer vision"

Connected through hyperedge HE-5 (induced by source paper "remote detection of idling cars using infrared imaging and deep networks"):
  with members:
    - "focal loss for dense object detection"
    - "improving generalization performance by switching from adam to sgd"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-6 (induced by source paper "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "security evaluation of pattern classifiers under attack"
    - "practical black box attacks against machine learning"
    - "concrete problems in ai safety"
    - "stealing machine learning models via prediction apis"
    - "convolutional two stream network fusion for video action recognition"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-7 (induced by source paper "word2visualvec image and video to sentence matching by visual feature prediction"):
  with members:
    - "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"
    - "jointly modeling embedding and translation to bridge video and language"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "deep residual learning for image recognition"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"

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)