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: "generalized intersection over union a metric and a loss for bounding box regression"
  Abstract: Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

Connected through hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "reppoints point set representation for object detection"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"
    - "deeper and wider siamese networks for real time visual tracking"

Connected through hyperedge HE-2 (induced by source paper "inferring spatial uncertainty in object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "iou loss for 2d 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "distance iou loss faster and better learning for bounding box regression"
    - "can we trust you on calibration of a probabilistic object detector for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "alpha n shortest path finder automated delivery robot with obstacle detection and avoiding system"):
  with members:
    - "you only look once unified real time object detection"
    - "monocular slam supported object recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "towards accurate one stage object detection with ap loss"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-5 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "development of real time adas object detector for deployment on cpu"

Connected through hyperedge HE-6 (induced by source paper "reppoints point set representation for object detection"):
  with members:
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "metaanchor learning to detect objects with customized anchors"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (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"
    - "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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "mmdetection open mmlab detection toolbox and benchmark"):
  with members:
    - "double head rcnn rethinking classification and localization for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "region proposal by guided anchoring"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "simpledet a simple and versatile distributed framework for object detection and instance recognition"
    - "feature selective anchor free module for single shot object detection"
    - "mask scoring r cnn"

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)