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: "what is holding back convnets for detection"
  Abstract: Convolutional neural networks have recently shown excellent results in general object detection and many other tasks. Albeit very effective, they involve many user-defined design choices. In this paper we want to better understand these choices by inspecting two key aspects "what did the network learn?", and "what can the network learn?". We exploit new annotations (Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite common belief, our results indicate that existing state-of-the-art convnet architectures are not invariant to various appearance factors. In fact, all considered networks have similar weak points which cannot be mitigated by simply increasing the training data (architectural changes are needed). We show that overall performance can improve when using image renderings for data augmentation. We report the best known results on the Pascal3D+ detection and view-point estimation tasks.

Connected through hyperedge HE-1 (induced by source paper "synthetic examples improve generalization for rare classes"):
  with members:
    - "low shot learning from imaginary 3d model"
    - "class balanced loss based on effective number of samples"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "learning category specific mesh reconstruction from image collections"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "recognition in terra incognita"

Connected through hyperedge HE-2 (induced by source paper "virtual training for a real application accurate object robot relative localization without calibration"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "cad2rl real single image flight without a single real image"
    - "play and learn using video games to train computer vision models"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "playing for data ground truth from computer games"
    - "single image 3d interpreter network"

Connected through hyperedge HE-3 (induced by source paper "generative model with coordinate metric learning for object recognition based on 3d models"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "single image tomography 3d volumes from 2d cranial x rays"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "unsupervised learning of 3d structure from images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "deep reflectance maps"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-5 (induced by source paper "marr revisited 2d 3d alignment via surface normal prediction"):
  with members:
    - "holistically nested edge detection"
    - "semantic pose using deep networks trained on synthetic rgb d"
    - "understanding deep features with computer generated imagery"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "predicting complete 3d models of indoor scenes"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-6 (induced by source paper "how useful is photo realistic rendering for visual learning"):
  with members:
    - "deep image scaling up image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "densebox unifying landmark localization with end to end object detection"):
  with members:
    - "learning to segment object candidates"
    - "multi view face detection using deep convolutional neural networks"
    - "parsenet looking wider to see better"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "looking outside of the box object detection and localization with multi scale patterns"):
  with members:
    - "holistically nested edge detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "fast r cnn"
    - "convolutional channel features"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)