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: "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
  Abstract: Online multi-object tracking (MOT) is a challenging problem and has many important applications including intelligence surveillance, robot navigation, and autonomous driving. In existing MOT methods, individual object’s movements and inter-object relations are mostly modeled separately and relations between them are still manually tuned. In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting. To tackle those difficulties, in this paper, we propose a deep continuous conditional random field (DCCRF) for solving the online MOT problem in a track-by-detection framework. The DCCRF consists of unary and pairwise terms. The unary terms estimate tracked objects’ displacements across time based on visual appearance information. They are modeled as deep convolution neural networks, which are able to learn discriminative visual features for tracklet association. The asymmetric pairwise terms model inter-object relations in an asymmetric way, which encourages high-confidence tracklets to help correct errors of low-confidence tracklets and not to be affected by low-confidence ones much. The DCCRF is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of our proposed DCCRF on two public benchmarks demonstrate the effectiveness of our proposed MOT framework.

Connected through hyperedge HE-1 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "cornernet detecting objects as paired keypoints"
    - "deep cosine metric learning for person re identification"
    - "event based moving object detection and tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "robust multi modality multi object tracking"):
  with members:
    - "deep multi sensor lane detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "combined image and world space tracking in traffic scenes"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"

Connected through hyperedge HE-3 (induced by source paper "fishnet a versatile backbone for image region and pixel level prediction"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "i revnet deep invertible networks"
    - "question guided hybrid convolution for visual question answering"
    - "deep layer aggregation"
    - "learning feature pyramids for human pose estimation"
    - "dilated residual networks"
    - "multi scale dense networks for resource efficient image classification"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "online multiple pedestrian tracking using deep temporal appearance matching association"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "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"
    - "eliminating exposure bias and loss evaluation mismatch in multiple object tracking"

Connected through hyperedge HE-5 (induced by source paper "end to end learning deep crf models for multi 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"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "towards a principled integration of multi camera re identification and tracking through optimal bayes filters"
    - "in defense of the triplet loss for person re identification"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-6 (induced by source paper "instance aware representation learning and association for online multi person tracking"):
  with members:
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "learning by tracking siamese cnn for robust target association"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "training region based object detectors with online hard example mining"
    - "performance measures and a data set for multi target multi camera tracking"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"):
  with members:
    - "a hybrid data association framework for robust online multi object tracking"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "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"
    - "mot16 a benchmark for multi object 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"

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)