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: "unrealstereo controlling hazardous factors to analyze stereo vision"
  Abstract: A reliable stereo algorithm is critical for many robotics applications. But textureless and specular regions can easily cause failure by making feature matching difficult. Understanding whether an algorithm is robust to these hazardous regions is important. Although many stereo benchmarks have been developed to evaluate performance, it is hard to quantify the effect of hazardous regions in real images because the location and severity of these regions are unknown. In this paper, we develop a synthetic image generation tool enabling to control hazardous factors, such as making objects more specular or transparent, to produce hazardous regions at different degrees. The densely controlled sampling strategy in virtual worlds enables to effectively stress test stereo algorithms by varying the types and degrees of the hazard. We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions. The observations from synthetic images are further validated by annotating hazardous regions in real-world datasets Middlebury and KITTI (which gives a sparse sampling of the hazards). Our synthetic image generation tool is based on a game engine Unreal Engine 4 and will be open-source along with the virtual scenes in our experiments. Many publicly available realistic game contents can be used by our tool to provide an enormous resource for development and evaluation of algorithms.

Connected through hyperedge HE-1 (induced by source paper "rsa randomized simulation as augmentation for robust human action recognition"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "virtualhome simulating household activities via programs"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-2 (induced by source paper "blendedmvs a large scale dataset for generalized multi view stereo networks"):
  with members:
    - "the replica dataset a digital replica of indoor spaces"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "point based multi view stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "deepmvs learning multi view stereopsis"
    - "megadepth learning single view depth prediction from internet photos"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-3 (induced by source paper "training object detectors on synthetic images containing reflecting materials"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "object detection using domain randomization and generative adversarial refinement of synthetic images"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "falling things a synthetic dataset for 3d object detection and pose estimation"
    - "focal loss for dense object detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "on pre trained image features and synthetic images for deep learning"
    - "unrealcv connecting computer vision to unreal engine"

Connected through hyperedge HE-4 (induced by source paper "identity preserve transform understand what activity classification models have learnt"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "videos as space time region graphs"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "adversarial attacks beyond the image space"
    - "mask r cnn"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-5 (induced by source paper "watermark retrieval from 3d printed objects via synthetic data training"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "watermark retrieval from 3d printed objects via convolutional neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "unrealcv connecting computer vision to unreal engine"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "synthetic data for text localisation in natural images"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep nets what have they ever done for vision"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the elephant in the room"
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "few shot image recognition by predicting parameters from activations"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-7 (induced by source paper "deep object pose estimation for semantic robotic grasping of household objects"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "falling things a synthetic dataset for 3d object detection and pose estimation"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "training deep networks with synthetic data bridging the reality gap by domain randomization"):
  with members:
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "on pre trained image features and synthetic images for deep learning"
    - "unrealcv connecting computer vision to unreal engine"

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)