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: "sinet a scale insensitive convolutional neural network for fast vehicle detection"
  Abstract: Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN-based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects and 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.

Connected through hyperedge HE-1 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "object detection based on region decomposition and assembly"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet lite efficient keypoint based object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "self training with noisy student improves imagenet classification"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "attribute guided feature learning network for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "cross entropy adversarial view adaptation for person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"

Connected through hyperedge HE-3 (induced by source paper "learning to separate detecting heavily occluded objects in urban scenes"):
  with members:
    - "double anchor r cnn for human detection in a crowd"
    - "learning a layout transfer network for context aware object detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "bounding box regression with uncertainty for accurate object detection"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-4 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "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"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-5 (induced by source paper "eliminating cross camera bias for vehicle re identification"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"

Connected through hyperedge HE-6 (induced by source paper "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "receptive field block net for accurate and fast object detection"
    - "single shot refinement neural network for object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-7 (induced by source paper "vehicle detection in deep learning"):
  with members:
    - "3d bounding box estimation using deep learning and geometry"
    - "xgboost a scalable tree boosting system"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "an empirical evaluation of deep learning on highway driving"
    - "you only look once unified real time object detection"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "orientation aware object detection with application to firearms"):
  with members:
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "perceptual generative adversarial networks for small object detection"
    - "mask r cnn"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"
    - "finding tiny faces"

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)