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: "pushing the limits of deep cnns for pedestrian detection"
  Abstract: Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophisticated deep CNN models, with a number of hand-crafted features, or explicit occlusion handling mechanism. In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional neural network (DCNN) model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labelling may be simply combined with a detector to boost the detection performance. By adding complementary hand-crafted features such as optical flow, the DCNN based detector can be further improved. We set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from $11.7\%$ to $8.9\%$, a relative improvement of $24\%$. We also achieve a comparable result to the state-of-the-art approaches on the 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"
    - "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-3 (induced by source paper "illumination aware faster r cnn for robust multispectral pedestrian detection"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "citypersons a diverse dataset for pedestrian detection"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "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"
    - "amodal instance segmentation"
    - "you only look once unified real time object detection"
    - "filtered channel features 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)