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: "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
  Abstract: Multiple object tracking has been a challenging field, mainly due to noisy detection sets an identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models that are built on an individual or several selected frames for the comparison of features, but they cannot encode the long-term appearance changes caused by pose, viewing angle, and lighting conditions. In this paper, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by the adaptive update and spatial weighting schemes. To handle occlusion and nearby objects that are sharing a similar appearance, we also design the cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. In addition, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state of the art on the  MOTChallenge  3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state of the art on the 2D benchmark of  MOTChallenge .

Connected through hyperedge HE-1 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision"):
  with members:
    - "video instance segmentation"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "centernet keypoint triplets for object detection"
    - "tracking without bells and whistles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "yolov3 an incremental improvement"
    - "exploit the connectivity multi object tracking with trackletnet"

Connected through hyperedge HE-2 (induced by source paper "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi level factorisation net for person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "the devil is in the middle exploiting mid level representations for cross domain instance matching"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "pedestrian tracking by probabilistic data association and correspondence embeddings"):
  with members:
    - "deep person re identification for probabilistic data association in multiple pedestrian tracking"
    - "resource aware person re identification across multiple resolutions"
    - "citypersons a diverse dataset for pedestrian detection"
    - "deep metric learning with angular loss"
    - "mask r cnn"
    - "mot16 a benchmark for multi object tracking"
    - "how far are we from solving pedestrian detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "using panoramic videos for multi person localization and tracking in a 3d panoramic coordinate"):
  with members:
    - "pifpaf composite fields for human pose estimation"
    - "monoloco monocular 3d pedestrian localization and uncertainty estimation"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "simple online and realtime tracking with a deep association metric"

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)