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: "pixel wise segmentation of street with neural networks"
  Abstract: Pixel-wise street segmentation of photographs taken from a drivers perspective is important for self-driving cars and can also support other object recognition tasks. A framework called SST was developed to examine the accuracy and execution time of different neural networks. The best neural network achieved an $F_1$-score of 89.5% with a simple feedforward neural network which trained to solve a regression task.

Connected through hyperedge HE-1 (induced by source paper "a game based approximate verification of deep neural networks with provable guarantees"):
  with members:
    - "scaling provable adversarial defenses"
    - "concolic testing for deep neural networks"
    - "global robustness evaluation of deep neural networks with provable guarantees for l0 norm"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient neural network robustness certification with general activation functions"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "optical flow with semantic segmentation and localized layers"
    - "convolutional random walk networks for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "feature guided black box safety testing of deep neural networks"):
  with members:
    - "reachability analysis of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "simple black box adversarial perturbations for deep networks"
    - "practical black box attacks against machine learning"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-4 (induced by source paper "lane boundary extraction from satellite imagery"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "aerial image geolocalization from recognition and matching of roads and intersections"
    - "an empirical evaluation of deep learning on highway driving"
    - "recurrent convolutional neural networks for scene parsing"
    - "vision based road detection in automotive systems a real time expectation driven approach"

Connected through hyperedge HE-5 (induced by source paper "stfcn spatio temporal fcn for semantic video segmentation"):
  with members:
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "deep hierarchical parsing for semantic segmentation"
    - "flownet learning optical flow with convolutional networks"
    - "attention to scale scale aware semantic image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "grid long short term memory"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a survey of semantic segmentation"):
  with members:
    - "instance aware semantic segmentation via multi task network cascades"
    - "conditional random fields as recurrent neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "recurrent convolutional neural networks for scene parsing"
    - "an improved objective evaluation measure for border detection in dermoscopy images"

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)