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 with spatially pooled features and structured ensemble learning"
  Abstract: Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. #R##N#In order to achieve a high object detection performance, we propose a new approach to extract low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.

Connected through hyperedge HE-1 (induced by source paper "part level convolutional neural networks for pedestrian detection using saliency and boundary box alignment"):
  with members:
    - "how far are we from solving pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "yolo9000 better faster stronger"
    - "scale aware fast r cnn for pedestrian detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "filtered channel features for pedestrian detection"
    - "learning deep features for discriminative localization"
    - "taking a deeper look at pedestrians"

Connected through hyperedge HE-2 (induced by source paper "robustness analysis of pedestrian detectors for surveillance"):
  with members:
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "how far are we from solving pedestrian detection"
    - "ten years of pedestrian detection what have we learned"
    - "pedestrian detection aided by deep learning semantic tasks"

Connected through hyperedge HE-3 (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-4 (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"

Connected through hyperedge HE-5 (induced by source paper "to boost or not to boost on the limits of boosted trees for object detection"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "how far are we from solving pedestrian detection"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "multi view face detection using deep convolutional neural networks"
    - "learning to detect vehicles by clustering appearance patterns"
    - "filtered channel features for pedestrian detection"
    - "occlusion coherence detecting and localizing occluded faces"
    - "a deep pyramid deformable part model for face detection"

Connected through hyperedge HE-6 (induced by source paper "pushing the limits of deep cnns for pedestrian detection"):
  with members:
    - "filtered channel features for pedestrian detection"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "convolutional channel features"
    - "object detection networks on convolutional feature maps"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "discriminatively trained and or graph models for object shape detection"
    - "learning complexity aware cascades for deep pedestrian detection"

Connected through hyperedge HE-7 (induced by source paper "3d object proposals using stereo imagery for accurate object class detection"):
  with members:
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "fast detection of multiple objects in traffic scenes with a common detection framework"
    - "what makes for effective detection proposals"
    - "sequential optimization for efficient high quality object proposal generation"
    - "learning to detect vehicles by clustering appearance patterns"
    - "filtered channel features for pedestrian detection"
    - "towards scene understanding with detailed 3d object representations"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "a unified multi scale deep convolutional neural network for fast object detection"):
  with members:
    - "what makes for effective detection proposals"
    - "holistically nested edge detection"
    - "you only look once unified real time object detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "filtered channel features for pedestrian detection"
    - "segdeepm exploiting segmentation and context in deep neural networks for object 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)