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: "circulant temporal encoding for video retrieval and temporal alignment"
  Abstract: We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene.

Connected through hyperedge HE-1 (induced by source paper "srg snippet relatedness based temporal action proposal generator"):
  with members:
    - "learning blind video temporal consistency"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "cbam convolutional block attention module"
    - "precise temporal action localization by evolving temporal proposals"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "behavior discovery and alignment of articulated object classes from unstructured video"):
  with members:
    - "articulated motion discovery using pairs of trajectories"
    - "associative embeddings for large scale knowledge transfer with self assessment"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "learning robust video synchronization without annotations"):
  with members:
    - "gated siamese convolutional neural network architecture for human re identification"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "image to image translation with conditional adversarial networks"
    - "spatial transformer networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "bag of attributes for video event retrieval"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

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)