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: "strengthening the effectiveness of pedestrian detection with spatially pooled features"
  Abstract: We propose a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art. Our new features are built on the basis of low-level visual features and spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. We then directly optimise the partial area under the ROC curve (\pAUC) measure, which concentrates detection performance in the range of most practical importance. The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets. We advance state-of-the-art results by lowering the average miss rate from $13\%$ to $11\%$ on the INRIA benchmark, $41\%$ to $37\%$ on the ETH benchmark, $51\%$ to $42\%$ on the TUD-Brussels benchmark and $36\%$ to $29\%$ on the Caltech-USA benchmark.

Connected through hyperedge HE-1 (induced by source paper "pedestrian detection the elephant in the room"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "deep high resolution representation learning for visual recognition"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"

Connected through hyperedge HE-2 (induced by source paper "widerperson a diverse dataset for dense pedestrian detection in the wild"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "occlusion aware r cnn detecting pedestrians in a crowd"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "single shot refinement neural network for object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "faceboxes a cpu real time face detector with high accuracy"

Connected through hyperedge HE-4 (induced by source paper "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"):
  with members:
    - "yolov3 an incremental improvement"
    - "mv yolo motion vector aided tracking by semantic object detection"
    - "msr net low light image enhancement using deep convolutional network"
    - "focal loss for dense object detection"
    - "deep bilateral learning for real time image enhancement"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "dehazenet an end to end system for single image haze removal"
    - "multinet real time joint semantic reasoning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "a content based late fusion approach applied to pedestrian detection"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "ten years of pedestrian detection what have we learned"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-6 (induced by source paper "perceptual generative adversarial networks for small object detection"):
  with members:
    - "how far are we from solving pedestrian detection"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (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"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning cross modal deep representations for robust pedestrian detection"):
  with members:
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "is faster r cnn doing well for pedestrian detection"
    - "scale aware fast r cnn for pedestrian detection"
    - "holistically nested edge detection"
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"
    - "learning deep representations of appearance and motion for anomalous event 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)