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: "cylks unsupervised cycle lucas kanade network for landmark tracking"
  Abstract: Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance. In contrast, the Lucas-Kanade (LK) algorithm works well without any annotation. However, LK has a strong assumption of photometric (brightness) consistency on image intensity and is easy to drift because of large motion, occlusion, and aperture problem. To relax the assumption and alleviate the drift problem, we propose CyLKs, a data-driven way of training Lucas-Kanade in an unsupervised manner. CyLKs learns a feature transformation through CNNs, transforming the input images to a feature space which is especially favorable to LK tracking. During training, we perform differentiable Lucas-Kanade forward and backward on the convolutional feature maps, and then minimize the re-projection error. During testing, we perform the LK tracking on the learned features. We apply our model to the task of landmark tracking and perform experiments on datasets of THUMOS and 300VW.

Connected through hyperedge HE-1 (induced by source paper "a baseline for 3d multi object tracking"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "how to train your deep multi object tracker"
    - "self supervised adaptation of high fidelity face models for monocular performance tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deepmot a differentiable framework for training multiple object trackers"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "fantrack 3d multi object tracking with feature association network"
    - "future near collision prediction from monocular video feasibility dataset and challenges"

Connected through hyperedge HE-2 (induced by source paper "groundnet monocular ground plane normal estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "real time correlation tracking via joint model compression and transfer"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "groundnet segmentation aware monocular ground plane estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

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)