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: "large scale fast and accurate shot boundary detection through spatio temporal convolutional neural networks"
  Abstract: Shot boundary detection (SBD) is an important pre-processing step for video manipulation. Here, each segment of frames is classified as either sharp, gradual or no transition. Current SBD techniques analyze hand-crafted features and attempt to optimize both detection accuracy and processing speed. However, the heavy computations of optical flow prevents this. To achieve this aim, we present an SBD technique based on spatio-temporal Convolutional Neural Networks (CNN). Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3.5 million frames of sharp and gradual transitions. The transitions are generated synthetically using image compositing models. Our dataset contain additional 70,000 frames of important hard-negative no transitions. We perform the largest evaluation to date for one SBD algorithm, on real and synthetic data, containing more than 4.85 million frames. In comparison to the state of the art, we outperform dissolve gradual detection, generate competitive performance for sharp detections and produce significant improvement in wipes. In addition, we are up to 11 times faster than the state of the art.

Connected through hyperedge HE-1 (induced by source paper "rekall specifying video events using compositions of spatiotemporal labels"):
  with members:
    - "fast video shot transition localization with deep structured models"
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "blazeit fast exploratory video queries using neural networks"
    - "scanner efficient video analysis at scale"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "prototypical networks for few shot learning"
    - "inferring generative model structure with static analysis"

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"
    - "ridiculously fast shot boundary detection with fully 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 "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-4 (induced by source paper "ridiculously fast shot boundary detection with fully convolutional neural networks"):
  with members:
    - "untrimmednets for weakly supervised action recognition and detection"
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "tensorflow a system for large scale machine learning"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "fully convolutional networks for semantic segmentation"

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)