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: "a metric for sets of trajectories that is practical and mathematically consistent"
  Abstract: Metrics on the space of sets of trajectories are important for scientists in the field of computer vision, machine learning, robotics, and general artificial intelligence. However, existing notions of closeness between sets of trajectories are either mathematically inconsistent or of limited practical use. In this paper, we outline the limitations in the current mathematically-consistent metrics, which are based on OSPA (Schuhmacher et al. 2008); and the inconsistencies in the heuristic notions of closeness used in practice, whose main ideas are common to the CLEAR MOT measures (Keni and Rainer 2008) widely used in computer vision. In two steps, we then propose a new intuitive metric between sets of trajectories and address these limitations. First, we explain a solution that leads to a metric that is hard to compute. Then we modify this formulation to obtain a metric that is easy to compute while keeping the useful properties of the previous metric. Our notion of closeness is the first demonstrating the following three features: the metric 1) can be quickly computed, 2) incorporates confusion of trajectories' identity in an optimal way, and 3) is a metric in the mathematical sense.

Connected through hyperedge HE-1 (induced by source paper "track to reconstruct and reconstruct to track"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mots multi object tracking and segmentation"
    - "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"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "robust dense mapping for large scale dynamic environments"
    - "premvos proposal generation refinement and merging for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "globally consistent multi people tracking using motion patterns"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "simple online and realtime tracking"
    - "online multi target tracking using recurrent neural networks"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "performance measures and a data set for multi target multi camera tracking"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"
    - "understanding trajectory behavior a motion pattern approach"
    - "near online multi target tracking with aggregated local flow descriptor"

Connected through hyperedge HE-3 (induced by source paper "multiple target tracking based on sets of trajectories"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "generalized optimal sub pattern assignment metric"
    - "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"

Connected through hyperedge HE-4 (induced by source paper "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "generalized optimal sub pattern assignment metric"
    - "multiple target tracking based on sets of trajectories"
    - "an efficient variational approximation of the best fitting multi bernoulli filter"

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)