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: "exploring human vision driven features for pedestrian detection"
  Abstract: Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are the first to design a local statistical multichannel descriptor to incorporate both color and gradient information. Second, we introduce a multidirection and multiscale contrast scheme based on grid cells to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons with respect to statistical descriptors, contrast measurements, and scale structures. By this way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.

Connected through hyperedge HE-1 (induced by source paper "pedestrian detection inspired by appearance constancy and shape symmetry"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "learning multilayer channel features for pedestrian detection"
    - "how far are we from solving pedestrian detection"
    - "scale aware fast r cnn for pedestrian detection"
    - "filtered channel features for pedestrian detection"
    - "convolutional channel features"
    - "learning complexity aware cascades for deep pedestrian detection"
    - "taking a deeper look at pedestrians"

Connected through hyperedge HE-2 (induced by source paper "how far are we from solving pedestrian detection"):
  with members:
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"
    - "taking a deeper look at pedestrians"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "strengthening the effectiveness of pedestrian detection with spatially pooled features"
    - "ten years of pedestrian detection what have we learned"
    - "local decorrelation for improved detection"
    - "pedestrian detection aided by deep learning semantic tasks"

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)