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: "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation"
  Abstract: A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and temporal feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.

Connected through hyperedge HE-1 (induced by source paper "hybrid channel based pedestrian detection"):
  with members:
    - "pcn part and context information for pedestrian detection with cnns"
    - "manifold adversarial learning"
    - "fused deep neural networks for efficient pedestrian detection"
    - "exploring multi branch and high level semantic networks for improving pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "what can help pedestrian detection"
    - "learning multilayer channel features for pedestrian detection"
    - "ian the individual aggregation network for person search"

Connected through hyperedge HE-2 (induced by source paper "ssa cnn semantic self attention cnn for pedestrian detection"):
  with members:
    - "pcn part and context information for pedestrian detection with cnns"
    - "fused deep neural networks for efficient pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "squeeze and excitation networks"
    - "repulsion loss detecting pedestrians in a crowd"
    - "stacked hourglass networks for human pose estimation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "occluded pedestrian detection with visible iou and box sign predictor"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "feature selective networks for object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "an analysis of scale invariance in object detection snip"
    - "repulsion loss detecting pedestrians in a crowd"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-4 (induced by source paper "pedestrian detection with autoregressive network phases"):
  with members:
    - "deep feature pyramid reconfiguration for object detection"
    - "path aggregation network for instance segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "learned in translation contextualized word vectors"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "repulsion loss detecting pedestrians in a crowd"

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)