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: "gonet a semi supervised deep learning approach for traversability estimation"
  Abstract: We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of negative images depicting blocked and unsafe areas. This makes the proposed methods practical. Positive examples can be collected easily by simply operating a robot through traversable spaces, while obtaining negative examples is time consuming, costly, and potentially dangerous. Through extensive experiments and several demonstrations, we show that the proposed traversability estimation approaches are robust and can generalize to unseen scenarios. Further, we demonstrate that our methods are memory efficient and fast, allowing for real-time operation on a mobile robot with single or stereo fisheye cameras. As part of our contributions, we open-source two new datasets for traversability estimation. These datasets are composed of approximately 24h of videos from more than 25 indoor environments. Our methods outperform baseline approaches for traversability estimation on these new datasets.

Connected through hyperedge HE-1 (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"
    - "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"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep visual mpc policy learning for navigation"):
  with members:
    - "a neural network based model predictive control of three phase inverter with an output lc filter"
    - "a behavioral approach to visual navigation with graph localization networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "ivoa introspective vision for obstacle avoidance"):
  with members:
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "introspective perception learning to predict failures in vision systems"

Connected through hyperedge HE-5 (induced by source paper "gibson env real world perception for embodied agents"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-6 (induced by source paper "vunet dynamic scene view synthesis for traversability estimation using an rgb camera"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "gibson env real world perception for embodied agents"
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "stochastic variational video prediction"
    - "future frame prediction for anomaly detection a new baseline"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "car net clairvoyant attentive recurrent network"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "vunet dynamic scene view synthesis for traversability estimation using an rgb camera"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "to go or not to go a near unsupervised learning approach for robot navigation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "diversified visual attention networks for fine grained object classification"
    - "image captioning with semantic attention"
    - "recurrent attention models for depth based person identification"

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)