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: "find your own way weakly supervised segmentation of path proposals for urban autonomy"
  Abstract: We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.

Connected through hyperedge HE-1 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "self driving cars a survey"
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "a parametric top view representation of complex road scenes"
    - "pixels to graphs by associative embedding"

Connected through hyperedge HE-2 (induced by source paper "measuring the utilization of public open spaces by deep learning a benchmark study at the detroit riverfront"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fcos fully convolutional one stage object detection"
    - "object detection with deep learning a review"
    - "detecting and recognizing human object interactions"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "multiple human parsing in the wild"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "safe robot navigation via multi modal anomaly detection"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "anomaly detection using deep learning based image completion"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "density estimation using real nvp"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "tackling existence probabilities of objects with motion planning for automated urban driving"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "limited visibility and uncertainty aware motion planning for automated driving"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-5 (induced by source paper "off road drivable area extraction using 3d lidar data"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "fully convolutional networks for semantic segmentation"
    - "real time detection of lane markers in urban streets"

Connected through hyperedge HE-6 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "variational end to end navigation and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-7 (induced by source paper "distant vehicle detection using radar and vision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "soft sampling for robust object detection"
    - "faster bounding box annotation for object detection in indoor scenes"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-8 (induced by source paper "self supervised visual terrain classification from unsupervised acoustic feature learning"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "fast scnn fast semantic segmentation network"
    - "modular sensor fusion for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning long range perception using self supervision from short range sensors and odometry"

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.RO(Robotics)