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: "object detection tracking and motion segmentation for object level video segmentation"
  Abstract: We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.

Connected through hyperedge HE-1 (induced by source paper "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "learning a discriminative feature network for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "robust tumor localization with pyramid grad cam"

Connected through hyperedge HE-2 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"

Connected through hyperedge HE-3 (induced by source paper "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "neurall towards a unified model for visual perception in automated driving"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"

Connected through hyperedge HE-4 (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-5 (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"
    - "modnet moving object detection network with motion and appearance for autonomous driving"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bottom up top down cues for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using motion cues"
    - "video scene parsing with predictive feature learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised semantic segmentation using web crawled videos"):
  with members:
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "learning semantic segmentation with weakly annotated videos"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "webly supervised learning of convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "modnet moving object detection network with motion and appearance for autonomous driving"):
  with members:
    - "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"
    - "learning motion patterns in videos"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "end to end learning for self driving cars"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

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)