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: "unsupervised object discovery and tracking in video collections"
  Abstract: This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates successive similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision.

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "activity driven weakly supervised object detection"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "joint discovery of object states and manipulation actions"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "bayesian semantic instance segmentation in open set world"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "unsupervised hard example mining from videos for improved object detection"
    - "maskrnn instance level video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "online object representations with contrastive learning"):
  with members:
    - "self supervisory signals for object discovery and detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "superpoint self supervised interest point detection and description"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "object category learning and retrieval with weak supervision"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "deep image prior"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "geometry aware video object detection for static cameras"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "tube cnn modeling temporal evolution of appearance for object detection in video"
    - "object detection in video with spatiotemporal sampling networks"
    - "object detection in videos by high quality object linking"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "detect to track and track to detect"
    - "object detection from video tubelets with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "large scale object mining for object discovery from unlabeled video"):
  with members:
    - "unsupervised hard example mining from videos for improved object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "object category learning and retrieval with weak supervision"
    - "track then decide category agnostic vision based multi object tracking"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-7 (induced by source paper "unsupervised image matching and object discovery as optimization"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mask r cnn"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "t cnn tubelets with convolutional neural networks for object detection from videos"):
  with members:
    - "cost effective active learning for deep image classification"
    - "object detection in videos with tubelet proposal networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "seq nms for video object detection"
    - "craft objects from images"
    - "you only look once unified real time object detection"

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)