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: "poi multiple object tracking with high performance detection and appearance feature"
  Abstract: Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available. In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version.

Connected through hyperedge HE-1 (induced by source paper "refinements in motion and appearance for online multi object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning a neural solver for multiple object tracking"
    - "resolution invariant person re identification"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "yolov3 an incremental improvement"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "citypersons a diverse dataset for pedestrian detection"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "mot16 a benchmark for multi object tracking"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-3 (induced by source paper "mpm joint representation of motion and position map for cell tracking"):
  with members:
    - "joint multi frame detection and segmentation for multi cell tracking"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "end to end representation learning for correlation filter based tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "r cnns for pose estimation and action detection"

Connected through hyperedge HE-4 (induced by source paper "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "exploiting offset guided network for pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint flow temporal flow fields for multi person tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"

Connected through hyperedge HE-5 (induced by source paper "refinedetlite a lightweight one stage object detection framework for cpu only devices"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "searching for mobilenetv3"
    - "consistent optimization for single shot object detection"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bounding box regression with uncertainty for accurate object detection"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "state aware re identification feature for multi target multi camera tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "learning non uniform hypergraph for multi object tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-7 (induced by source paper "towards real time multi object tracking"):
  with members:
    - "tracking without bells and whistles"
    - "mots multi object tracking and segmentation"
    - "online multi object tracking with dual matching attention networks"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "multi task learning as multi objective optimization"
    - "in defense of the triplet loss for person re identification"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-8 (induced by source paper "occlusion robust online multi object visual tracking using a gm phd filter with a cnn based re identification"):
  with members:
    - "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "region based quality estimation network for large scale person re identification"

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)