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: "sam rcnn scale aware multi resolution multi channel pedestrian detection"
  Abstract: Convolutional neural networks (CNN) have enabled significant improvements in pedestrian detection owing to the strong representation ability of the CNN features. Recently, aggregating features from multiple layers of a CNN has been considered as an effective approach, however, the same approach regarding feature representation is used for detecting pedestrians of varying scales. Consequently, it is not guaranteed that the feature representation for pedestrians of a particular scale is optimised. In this paper, we propose a Scale-Aware Multi-resolution (SAM) method for pedestrian detection which can adaptively select multi-resolution convolutional features according to pedestrian sizes. The proposed SAM method extracts the appropriate CNN features that have strong representation ability as well as sufficient feature resolution, given the size of the pedestrian candidate output from a region proposal network. Moreover, we propose an enhanced SAM method, termed as SAM+, which incorporates complementary features channels and achieves further performance improvement. Evaluations on the challenging Caltech and KITTI pedestrian benchmarks demonstrate the superiority of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "coupled network for robust pedestrian detection with gated multi layer feature extraction and deformable occlusion handling"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "couplenet coupling global structure with local parts for object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "repulsion loss detecting pedestrians in a crowd"
    - "learning multilayer channel features for pedestrian detection"

Connected through hyperedge HE-2 (induced by source paper "gated multi layer convolutional feature extraction network for robust pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "repulsion loss detecting pedestrians in a crowd"
    - "learning multilayer channel features for pedestrian detection"
    - "how far are we from solving pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"

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)