human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust multi modality multi object tracking"):
  Abstract: Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects. Most current approaches for multi-sensor multi-object tracking are either lack of reliability by tightly relying on a single input source (e.g., center camera), or not accurate enough by fusing the results from multiple sensors in post processing without fully exploiting the inherent information. In this study, we design a generic sensor-agnostic multi-modality MOT framework (mmMOT), where each modality (i.e., sensors) is capable of performing its role independently to preserve reliability, and further improving its accuracy through a novel multi-modality fusion module. Our mmMOT can be trained in an end-to-end manner, enables joint optimization for the base feature extractor of each modality and an adjacency estimator for cross modality. Our mmMOT also makes the first attempt to encode deep representation of point cloud in data association process in MOT. We conduct extensive experiments to evaluate the effectiveness of the proposed framework on the challenging KITTI benchmark and report state-of-the-art performance. Code and models are available at this https URL.

Includes members:
  Member "deep multi sensor lane detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aerial multi object tracking by detection using deep association networks")
  Member "fishnet a versatile backbone for image region and pixel level prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian detection the elephant in the room")
  Member "beyond pixels leveraging geometry and shape cues for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset")
  Member "end to end learning of multi sensor 3d tracking by detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "combined image and world space tracking in traffic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "the perfect match 3d point cloud matching with smoothed densities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")

Question: Please tell me which class the source paper 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)