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: "learning optimal parameters for multi target tracking with contextual interactions"
  Abstract: We describe an end-to-end framework for learning parameters of min-cost flow multi-target tracking problem with quadratic trajectory interactions including suppression of overlapping tracks and contextual cues about co-occurrence of different objects. Our approach utilizes structured prediction with a tracking-specific loss function to learn the complete set of model parameters. In this learning framework, we evaluate two different approaches to finding an optimal set of tracks under a quadratic model objective, one based on an linear program (LP) relaxation and the other based on novel greedy variants of dynamic programming that handle pairwise interactions. We find the greedy algorithms achieve almost equivalent accuracy to the LP relaxation while being up to 10$$\times $$× faster than a commercial LP solver. We evaluate trained models on three challenging benchmarks. Surprisingly, we find that with proper parameter learning, our simple data association model without explicit appearance/motion reasoning is able to achieve comparable or better accuracy than many state-of-the-art methods that use far more complex motion features or appearance affinity metric learning.

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 "how to train your deep multi object tracker"):
  with members:
    - "tracking without bells and whistles"
    - "mots multi object tracking and segmentation"
    - "online multi object tracking with dual matching attention networks"
    - "combined image and world space tracking in traffic scenes"
    - "distractor aware siamese networks for visual object tracking"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-3 (induced by source paper "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "learning non uniform hypergraph for multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "mask r cnn"

Connected through hyperedge HE-4 (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-5 (induced by source paper "online multi target tracking for maneuvering vehicles in dynamic road context"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "towards multi object detection and tracking in urban scenario under uncertainties"
    - "near online multi target tracking with aggregated local flow descriptor"

Connected through hyperedge HE-6 (induced by source paper "machine learning methods for solving assignment problems in multi target tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "combined image and world space tracking in traffic scenes"
    - "graph embedding techniques applications and performance a survey"
    - "carla an open urban driving simulator"
    - "a hybrid data association framework for robust online multi object tracking"
    - "deep reinforcement learning a brief survey"
    - "fusion of head and full body detectors for multi object tracking"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"

Connected through hyperedge HE-7 (induced by source paper "deep affinity network for multiple object tracking"):
  with members:
    - "learning non uniform hypergraph for multi object tracking"
    - "deep cosine metric learning for person re identification"
    - "benchmark data and method for real time people counting in cluttered scenes using depth sensors"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "learning regression and verification networks for long term visual tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "detect to track and track to detect"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-8 (induced by source paper "online multi object tracking with historical appearance matching and scene adaptive detection filtering"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

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)