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: "lost and found detecting small road hazards for self driving vehicles"
  Abstract: Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.

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 "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"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"

Connected through hyperedge HE-3 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

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 "detecting the unexpected via image resynthesis"):
  with members:
    - "lightweight probabilistic deep networks"
    - "learning a discriminative feature network for semantic segmentation"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "the fishyscapes benchmark measuring blind spots in semantic segmentation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "fusion volumetric object level slam"
    - "semantic foggy scene understanding with synthetic data"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "a benchmark for anomaly segmentation"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "learning rich features for image manipulation detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"
    - "deep anomaly detection with outlier exposure"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-8 (induced by source paper "efficacy of pixel level ood detection for semantic segmentation"):
  with members:
    - "bayesian semantic instance segmentation in open set world"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "discriminative out of distribution detection for semantic segmentation"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "enhancing the reliability of out of distribution image detection in neural networks"

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)