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: "constrained structured regression with convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision. If provided with enough training data, they predict almost any visual quantity. In a discrete setting, such as classification, CNNs are not only able to predict a label but often predict a confidence in the form of a probability distribution over the output space. In continuous regression tasks, such a probability estimate is often lacking. We present a regression framework which models the output distribution of neural networks. This output distribution allows us to infer the most likely labeling following a set of physical or modeling constraints. These constraints capture the intricate interplay between different input and output variables, and complement the output of a CNN. However, they may not hold everywhere. Our setup further allows to learn a confidence with which a constraint holds, in the form of a distribution of the constrain satisfaction. We evaluate our approach on the problem of intrinsic image decomposition, and show that constrained structured regression significantly increases the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "propel probabilistic parametric regression loss for convolutional neural networks"):
  with members:
    - "mask r cnn"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "human pose estimation via convolutional part heatmap regression"
    - "deep residual learning for image recognition"
    - "flowing convnets for human pose estimation in videos"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "estimating depth from monocular images as classification using deep fully convolutional residual networks"):
  with members:
    - "discriminative training of deep fully connected continuous crfs with task specific loss"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "unsupervised monocular depth estimation with left right consistency"
    - "bridging category level and instance level semantic image segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "learning non lambertian object intrinsics across shapenet categories"):
  with members:
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "inferring 3d object pose in rgb d images"
    - "u net convolutional networks for biomedical image segmentation"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"
    - "deep reflectance maps"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-4 (induced by source paper "what is around the camera"):
  with members:
    - "radiometric scene decomposition scene reflectance illumination and geometry from rgb d images"
    - "deep outdoor illumination estimation"
    - "novel views of objects from a single image"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "seeing behind the camera identifying the authorship of a photograph"
    - "deep reflectance maps"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-5 (induced by source paper "natural illumination from multiple materials using deep learning"):
  with members:
    - "novel views of objects from a single image"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "seeing behind the camera identifying the authorship of a photograph"
    - "shapenet an information rich 3d model repository"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "designing deep networks for surface normal estimation"
    - "depth map prediction from a single image using a multi scale deep network"

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)