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: "generic object detection with dense neural patterns and regionlets"
  Abstract: This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns, short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with the Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the original Regionlets approach without DNPs.

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 "deep regionlets blended representation and deep learning for generic object detection"):
  with members:
    - "an analysis of pre training on object detection"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "cornernet detecting objects as paired keypoints"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "deep feature pyramid reconfiguration for object detection"
    - "multi scale location aware kernel representation for object detection"
    - "pseudo mask augmented object detection"

Connected through hyperedge HE-4 (induced by source paper "residual networks of residual networks multilevel residual networks"):
  with members:
    - "weighted residuals for very deep networks"
    - "resnet in resnet generalizing residual architectures"
    - "deep residual networks with exponential linear unit"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "convolutional residual memory networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-5 (induced by source paper "fine grained age estimation in the wild with attention lstm networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "deep label distribution learning with label ambiguity"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "video summarization with attention based encoder decoder networks"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "pyramidal ror for image classification"
    - "apparent age estimation using ensemble of deep learning models"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-6 (induced by source paper "age group and gender estimation in the wild with deep ror architecture"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "dual path networks"
    - "identity mappings in deep residual networks"
    - "squared earth mover s distance based loss for training deep neural networks"
    - "densely connected convolutional networks"
    - "neural networks with smooth adaptive activation functions for regression"
    - "wide residual networks"
    - "deep pyramidal residual networks"

Connected through hyperedge HE-7 (induced by source paper "pyramidal ror for image classification"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "residual attention network for image classification"
    - "deep residual networks with exponential linear unit"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "deep pyramidal residual networks"

Connected through hyperedge HE-8 (induced by source paper "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"):
  with members:
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "learning deconvolution network for semantic segmentation"
    - "multimodal deep learning for robust rgb d object recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "caffe convolutional architecture for fast feature embedding"

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)