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: "pedestrian detection inspired by appearance constancy and shape symmetry"
  Abstract: Most state-of-the-art methods in pedestrian detection are unable to achieve a good trade-off between accuracy and efficiency. For example, ACF has a fast speed but a relatively low detection rate, while checkerboards have a high detection rate but a slow speed. Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features: side-inner difference features (SIDF) and symmetrical similarity features (SSFs). SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and its inner part. SSF can capture the symmetrical similarity of pedestrian shape. However, it is difficult for neighboring features to have such above characterization abilities. Finally, we propose to combine both non-neighboring features and neighboring features for pedestrian detection. It is found that non-neighboring features can further decrease the log-average miss rate by 4.44%. The relationship between our proposed method and some state-of-the-art methods is also given. Experimental results on INRIA, Caltech, and KITTI data sets demonstrate the effectiveness and efficiency of the proposed method. Compared with the state-of-the-art methods without using CNN, our method achieves the best detection performance on Caltech, outperforming the second best method (i.e., checkerboards) by 2.27%. Using the new annotations of Caltech, it can achieve 11.87% miss rate, which outperforms other methods.

Connected through hyperedge HE-1 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-2 (induced by source paper "exploring multi branch and high level semantic networks for improving pedestrian detection"):
  with members:
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "dssd deconvolutional single shot detector"
    - "dilated residual networks"
    - "scale aware face detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-3 (induced by source paper "too far to see not really pedestrian detection with scale aware localization policy"):
  with members:
    - "tree structured reinforcement learning for sequential object localization"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "scale aware fast r cnn for pedestrian detection"
    - "filtered channel features for pedestrian detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "robustness analysis of pedestrian detectors for surveillance"):
  with members:
    - "how far are we from solving pedestrian detection"
    - "ten years of pedestrian detection what have we learned"
    - "pedestrian detection with spatially pooled features and structured ensemble learning"
    - "pedestrian detection aided by deep learning semantic tasks"

Connected through hyperedge HE-5 (induced by source paper "learning multilayer channel features for pedestrian detection"):
  with members:
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "how far are we from solving pedestrian detection"
    - "scale aware pixelwise object proposal networks"
    - "convolution in convolution for network in network"
    - "scale aware fast r cnn for pedestrian detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "filtered channel features for pedestrian detection"

Connected through hyperedge HE-6 (induced by source paper "cascade learning by optimally partitioning"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "ten years of pedestrian detection what have we learned"
    - "training effective node classifiers for cascade classification"

Connected through hyperedge HE-7 (induced by source paper "convolution in convolution for network in network"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "cascaded subpatch networks for effective cnns"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "pcanet a simple deep learning baseline for image classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "scale aware fast r cnn for pedestrian detection"):
  with members:
    - "a real time pedestrian detector using deep learning for human aware navigation"
    - "how far are we from solving pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "attentive contexts for object detection"
    - "you only look once unified real time object detection"
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"

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)