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: "failing to learn autonomously identifying perception failures for self driving cars"
  Abstract: One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical application, such as autonomous driving, the error rates of the current state of the art are still too high to enable safe operation. Moreover, the characterization of object detector performance is primarily limited to testing on prerecorded datasets. Errors that occur on novel data go undetected without additional human labels. In this letter, we propose an automated method to identify mistakes made by object detectors  without ground truth labels . We show that inconsistencies in the object detector output between a pair of similar images can be used as hypotheses for false negatives (e.g., missed detections) and using a novel set of features for each hypothesis, an off-the-shelf binary classifier can be used to find valid errors. In particular, we study two distinct cues– temporal  and  stereo   inconsistencies—using data that are readily available on most autonomous vehicles. Our method can be used with any camera-based object detector and we illustrate the technique on several sets of real world data. We show that a state-of-the-art detector, tracker, and our classifier trained only on synthetic data can identify valid errors on KITTI tracking dataset with an average precision of 0.94. We also release a new tracking dataset with 104 sequences totaling 80,655 labeled pairs of stereo images along with ground truth disparity from a game engine to facilitate further research.

Connected through hyperedge HE-1 (induced by source paper "shadow transfer single image relighting for urban road scenes"):
  with members:
    - "a cyclically trained adversarial network for invariant representation learning"
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "online illumination invariant moving object detection by generative neural network"
    - "night to day image translation for retrieval based localization"
    - "geometric image synthesis"
    - "carla an open urban driving simulator"
    - "combogan unrestrained scalability for image domain translation"
    - "interpretable transformations with encoder decoder networks"

Connected through hyperedge HE-2 (induced by source paper "automated evaluation of semantic segmentation robustness for autonomous driving"):
  with members:
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "uncertainty aware driver trajectory prediction at urban intersections"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "variational end to end navigation and localization"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "autorally an open platform for aggressive autonomous driving"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "global optimality guarantees for nonconvex unsupervised video segmentation"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "background subtraction in real applications challenges current models and future directions"
    - "exact guarantees on the absence of spurious local minima for non negative rank 1 robust principal component analysis"
    - "how much restricted isometry is needed in nonconvex matrix recovery"
    - "matrix completion has no spurious local minimum"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "fully convolutional networks for semantic segmentation"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "ivoa introspective vision for obstacle avoidance"):
  with members:
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "introspective perception learning to predict failures in vision systems"

Connected through hyperedge HE-6 (induced by source paper "modeling camera effects to improve visual learning from synthetic data"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unsupervised domain adaptation for semantic segmentation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarially tuned scene generation"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "modeling camera effects to improve deep vision for real and synthetic data"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unsupervised domain adaptation for semantic segmentation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image de raining using a conditional generative adversarial network"
    - "adversarially tuned scene generation"
    - "the cityscapes dataset for semantic urban scene understanding"

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)