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: "deepmot a differentiable framework for training multiple object trackers"
  Abstract: Multiple Object Tracking accuracy and precision (MOTA and MOTP) are two standard and widely-used metrics to assess the quality of multiple object trackers. They are specifically designed to encode the challenges and difficulties of tracking multiple objects. To directly optimize a tracker based on MOTA and MOTP is difficult, since both the metrics are strongly rely on the Hungarian algorithm, which are non-differentiable. We propose a differentiable proxy for the MOTA and MOTP, thus allowing to train a deep multiple-object tracker by directly optimizing (a proxy of) the standard MOT metrics. The proposed approximation is based on a bidirectional recurrent network that inputs the object-to-hypothesis distance matrix and outputs the optimal hypothesis-to-object association, thus emulating the Hungarian algorithm. Followed by a differentiable module, the estimated association is used to compute the MOTA and MOTP. The experimental study demonstrates the benefits of this differentiable framework on two recent deep trackers over the MOT17 dataset. Moreover, the code is publicly available from this https URL.

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"
    - "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"
    - "mots multi object tracking and segmentation"

Connected through hyperedge HE-2 (induced by source paper "multiple object tracking with motion and appearance cues"):
  with members:
    - "how to train your deep multi object tracker"
    - "vision meets drones a challenge"
    - "yolov3 an incremental improvement"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "single shot refinement neural network for 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)