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: "learning to detect vehicles by clustering appearance patterns"
  Abstract: This paper studies efficient means for dealing with intra-category diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "a general pipeline for 3d detection of vehicles"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "seeing what is not there learning context to determine where objects are missing"
    - "dsod learning deeply supervised object detectors from scratch"
    - "foveanet perspective aware urban scene parsing"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-2 (induced by source paper "sinet a scale insensitive convolutional neural network for fast vehicle detection"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "3d bounding box estimation using deep learning and geometry"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (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"
    - "filtered channel features for pedestrian detection"
    - "occlusion coherence detecting and localizing occluded faces"
    - "a deep pyramid deformable part model for face detection"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-4 (induced by source paper "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "3d object class detection in the wild"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "development of a n type gm phd filter for multiple target multiple type visual tracking"):
  with members:
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "near online multi target tracking with aggregated local flow descriptor"
    - "multiple object tracking a review"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "tracking as a whole multi target tracking by modeling group behavior with sequential detection"):
  with members:
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"

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"
    - "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"
    - "fast r cnn"

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"
    - "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"
    - "faster r cnn towards real time object detection with region proposal networks"

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)