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: "free space detection with self supervised and online trained fully convolutional networks"
  Abstract: Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a self-supervised manner and achieve similar results compared to training on manually annotated data, thereby reducing the need for large manually annotated training sets. To this end, our self-supervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN. Additionally, our self-supervised training facilitates online training of the FCN instead of offline. Consequently, given that the applied FCN is relatively small, the free-space analysis becomes highly adaptive to any traffic scene that the vehicle encounters. We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper. Experiments show that the online training boosts performance with 5% when compared to offline training, both for Fmax and AP .

Connected through hyperedge HE-1 (induced by source paper "enhanced free space detection in multiple lanes based on single cnn with scene identification"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "lidar camera fusion for road detection using fully convolutional neural networks"
    - "towards end to end lane detection an instance segmentation approach"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "static free space detection with laser scanner using occupancy grid maps"):
  with members:
    - "multinet real time joint semantic reasoning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "robust fusion of lidar and wide angle camera data for autonomous mobile robots"):
  with members:
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "smart guiding glasses for visually impaired people in indoor environment"

Connected through hyperedge HE-4 (induced by source paper "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "lost and found detecting small road hazards for self driving vehicles"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "towards open set deep networks"
    - "going deeper with convolutions"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "optical flow based self supervised learning of obstacle appearance applied to mav landing"):
  with members:
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "orb slam a versatile and accurate monocular slam system"

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)