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: "modeling camera effects to improve deep vision for real and synthetic data"
  Abstract: Recent work has focused on generating synthetic imagery and augmenting real imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling the variation in the sensor domain. Unfortunately, varying sensor effects can degrade performance and generalizability of results for visual tasks trained on human annotated datasets. This paper proposes an efficient, automated physically-based augmentation pipeline to vary sensor effects -- specifically, chromatic aberration, blur, exposure, noise, and color cast -- across both real and synthetic imagery. In particular, this paper illustrates that augmenting training datasets with the proposed pipeline improves the robustness and generalizability of object detection on a variety of benchmark vehicle datasets.

Connected through hyperedge HE-1 (induced by source paper "a multi hypothesis approach to color constancy"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "color cerberus"
    - "what else can fool deep learning addressing color constancy errors on deep neural network performance"
    - "fast fourier color constancy and grayness index for ispa illumination estimation challenge"
    - "convolutional mean a simple convolutional neural network for illuminant estimation"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "unsupervised learning for color constancy"

Connected through hyperedge HE-2 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "spatially transformed adversarial examples"
    - "autoaugment learning augmentation policies from data"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unprocessing images for learned raw denoising"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "random erasing data augmentation"
    - "modelling the scene dependent imaging in cameras with a deep neural network"

Connected through hyperedge HE-3 (induced by source paper "adapting semantic segmentation models for changes in illumination and camera perspective"):
  with members:
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "soft prototyping camera designs for car detection based on a convolutional neural network"):
  with members:
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "multimodal unsupervised image to image translation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "autoaugment learning augmentation policies from data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"

Connected through hyperedge HE-6 (induced by source paper "silhonet an rgb method for 6d object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "a unified framework for multi view multi class object pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "global hypothesis generation for 6d object pose estimation"

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)