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: "yotube searching action proposal via recurrent and static regression networks"
  Abstract: In this paper, we propose  YoTube —a novel deep learning framework for generating action proposals in untrimmed videos, where each action proposal corresponds to a spatial-temporal tube that potentially locates one human action. Most of the existing works generate proposals by clustering low-level features or linking image proposals, which ignore the interplay between long-term temporal context and short-term cues. Different from these works, our method considers the interplay by designing a new recurrent  YoTube  detector and static  YoTube  detector. The recurrent  YoTube  detector sequentially regresses candidate bounding boxes using Recurrent Neural Network learned long-term temporal contexts. The static  YoTube  detector produces bounding boxes using rich appearance cues in every single frame. To fully exploit the complementary appearance, motion, and temporal context, we train the recurrent and static detector using RGB (Color) and flow information. Moreover, we fuse the corresponding outputs of the detectors to produce accurate and robust proposal boxes and obtain the final action proposals by linking the proposal boxes using dynamic programming with a novel path trimming method. Benefiting from the pipeline of our method, the untrimmed video could be effectively and efficiently handled. Extensive experiments on the challenging UCF-101, UCF-Sports, and JHMDB datasets show superior performance of the proposed method compared with the state of the arts.

Connected through hyperedge HE-1 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust principal component analysis on graphs"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-2 (induced by source paper "low rank kernel learning for graph based clustering"):
  with members:
    - "robust pca via nonconvex rank approximation"
    - "robust subspace clustering via smoothed rank approximation"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "temporally coherent video harmonization using adversarial networks"):
  with members:
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "adversarial spatio temporal learning for video deblurring"
    - "imagine this scripts to compositions to videos"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "dual motion gan for future flow embedded video prediction"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning through deterministic assignment of hidden parameters"):
  with members:
    - "distributed learning with regularized least squares"
    - "constructive neural network learning"
    - "deep kernel learning"

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)