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: "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
  Abstract: The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is used to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with relative performance gains of up to 50%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.

Connected through hyperedge HE-1 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "the curious case of adversarially robust models more data can help double descend or hurt generalization"):
  with members:
    - "more data can expand the generalization gap between adversarially robust and standard models"
    - "a new perspective for flexible feature gathering in scene text recognition via character anchor pooling"
    - "lower bounds for adversarially robust pac learning"
    - "two models of double descent for weak features"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "deep double descent where bigger models and more data hurt"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"

Connected through hyperedge HE-3 (induced by source paper "lidar guided small obstacle segmentation"):
  with members:
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "rethinking atrous convolution for semantic image segmentation"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "lost and found detecting small road hazards for self driving vehicles"

Connected through hyperedge HE-4 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-5 (induced by source paper "fooling a real car with adversarial traffic signs"):
  with members:
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "exploring the space of black box attacks on deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "small obstacle avoidance based on rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "incorporating depth into both cnn and crf for indoor semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "on assessing driver awareness of situational criticalities multi modal bio sensing and vision based analysis evaluations and insights"):
  with members:
    - "utilizing deep learning towards multi modal bio sensing and vision based affective computing"
    - "attention monitoring and hazard assessment with bio sensing and vision empirical analysis utilizing cnns on the kitti dataset"
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "multi modal approach for affective computing"
    - "imitating driver behavior with generative adversarial networks"
    - "identifying stable patterns over time for emotion recognition from eeg"
    - "an empirical evaluation of deep learning on highway driving"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "information measure similarity theory message importance measure via shannon entropy"):
  with members:
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "a large deviations approach to secure lossy compression"
    - "message importance measure and its application to minority subset detection in big data"

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)