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: "shape from shading through shape evolution"
  Abstract: In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark.

Connected through hyperedge HE-1 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-2 (induced by source paper "thinking outside the pool active training image creation for relative attributes"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generative adversarial active learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "active decision boundary annotation with deep generative models"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"

Connected through hyperedge HE-3 (induced by source paper "meshadv adversarial meshes for visual recognition"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "unsupervised training for 3d morphable model regression"
    - "generating adversarial examples with adversarial networks"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "learning from synthetic humans"

Connected through hyperedge HE-4 (induced by source paper "realistic adversarial examples in 3d meshes"):
  with members:
    - "spatially transformed adversarial examples"
    - "yolov3 an incremental improvement"
    - "unsupervised training for 3d morphable model regression"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "physical adversarial examples for object detectors"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-5 (induced by source paper "soseleto a unified approach to transfer learning and training with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "the resistance to label noise in k nn and cnn depends on its concentration"
    - "multi adversarial domain adaptation"
    - "on the resistance of neural nets to label noise"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "cycada cycle consistent adversarial domain adaptation"

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)