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: "modnet moving object detection network with motion and appearance for autonomous driving"
  Abstract: We propose a novel multi-task learning system that combines appearance and motion cues for a better semantic reasoning of the environment. A unified architecture for joint vehicle detection and motion segmentation is introduced. In this architecture, a two-stream encoder is shared among both tasks. In order to evaluate our method in autonomous driving setting, KITTI annotated sequences with detection and odometry ground truth are used to automatically generate static/dynamic annotations on the vehicles. This dataset is called KITTI Moving Object Detection dataset (KITTI MOD). The dataset will be made publicly available to act as a benchmark for the motion detection task. Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21.5% in mAP on KITTI MOD. Our method performs on par with the state of the art unsupervised methods on DAVIS benchmark for generic object segmentation. One of our interesting conclusions is that joint training of motion segmentation and vehicle detection benefits motion segmentation. Motion segmentation has relatively fewer data, unlike the detection task. However, the shared fusion encoder benefits from joint training to learn a generalized representation. The proposed method runs in 120 ms per frame, which beats the state of the art motion detection/segmentation in computational efficiency.

Connected through hyperedge HE-1 (induced by source paper "neurall towards a unified model for visual perception in automated driving"):
  with members:
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "yolov3 an incremental improvement"
    - "taskonomy disentangling task transfer learning"
    - "auxiliary tasks in multi task learning"
    - "learning efficient convolutional networks through network slimming"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-2 (induced by source paper "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hallucinating dense optical flow from sparse lidar for autonomous vehicles"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"

Connected through hyperedge HE-3 (induced by source paper "optical flow augmented semantic segmentation networks for automated driving"):
  with members:
    - "semantic video cnns through representation warping"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "sfm net learning of structure and motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning motion patterns in videos"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation by optical flow augmentation in semantic segmentation"):
  with members:
    - "optical flow augmented semantic segmentation networks for automated driving"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "playing for benchmarks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-5 (induced by source paper "multi stream cnn based video semantic segmentation for automated driving"):
  with members:
    - "rtseg real time semantic segmentation comparative study"
    - "the 2017 davis challenge on video object segmentation"
    - "demon depth and motion network for learning monocular stereo"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep residual learning for image recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "multi cue vehicle detection for semantic video compression in georegistered aerial videos"):
  with members:
    - "xview objects in context in overhead imagery"
    - "yolov3 an incremental improvement"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "real time dynamic object detection for autonomous driving using prior 3d maps"):
  with members:
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "complex yolo real time 3d object detection on point clouds"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "carla an open urban driving simulator"
    - "an end to end system for crowdsourced 3d maps for autonomous vehicles the mapping component"
    - "cnn for very fast ground segmentation in velodyne lidar data"
    - "exploring spatial context for 3d semantic segmentation of point clouds"

Connected through hyperedge HE-8 (induced by source paper "an efficient optical flow based motion detection method for non stationary scenes"):
  with members:
    - "uct learning unified convolutional networks for real time visual tracking"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning motion patterns in videos"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

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)