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: "ridiculously fast shot boundary detection with fully convolutional neural networks"
  Abstract: Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing. Previous work typically used a combination of low-level features like color histograms, in conjunction with simple models such as SVMs. Instead, we propose to learn shot detection end-to-end, from pixels to final shot boundaries. For training such a model, we rely on our insight that all shot boundaries are generated. Thus, we create a dataset with one million frames and automatically generated transitions such as cuts, dissolves and fades. In order to efficiently analyze hours of videos, we propose a Convolutional Neural Network (CNN) which is fully convolutional in time, thus allowing to use a large temporal context without the need to repeatedly processing frames. With this architecture our method obtains state-of-the-art results while running at an unprecedented speed of more than 120x real-time.

Connected through hyperedge HE-1 (induced by source paper "laeo net revisiting people looking at each other in videos"):
  with members:
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "learnable pins cross modal embeddings for person identity"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "fine grained head pose estimation without keypoints"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "ssd single shot multibox detector"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "anticipating visual representations from unlabeled video"

Connected through hyperedge HE-2 (induced by source paper "transnet a deep network for fast detection of common shot transitions"):
  with members:
    - "fast video shot transition localization with deep structured models"
    - "large scale fast and accurate shot boundary detection through spatio temporal convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"
    - "video shot boundary detection using motion activity descriptor"

Connected through hyperedge HE-3 (induced by source paper "phd gifs personalized highlight detection for automatic gif creation"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "self normalizing neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"

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"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-5 (induced by source paper "convolutional hashing for automated scene matching"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep hashing with category mask for fast video retrieval"
    - "deep hyperspherical learning"
    - "riemannian approach to batch normalization"
    - "hashnet deep learning to hash by continuation"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "transfer adversarial hashing for hamming space retrieval"
    - "video retrieval based on deep convolutional neural network"

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)