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: "g cnn an iterative grid based object detector"
  Abstract: We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.

Connected through hyperedge HE-1 (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"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "cascade r cnn high quality object detection and instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-3 (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-4 (induced by source paper "side aware boundary localization for more precise object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "switchable whitening for deep representation learning"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "nas fcos fast neural architecture search for object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-5 (induced by source paper "region proposal by guided anchoring"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "metaanchor learning to detect objects with customized anchors"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "toward scale invariance and position sensitive region proposal networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-6 (induced by source paper "residual objectness for imbalance reduction"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (induced by source paper "step spatio temporal progressive learning for video action detection"):
  with members:
    - "discovering spatio temporal action tubes"
    - "actor centric relation network"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "cascade r cnn delving into high quality object detection"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-8 (induced by source paper "fa rpn floating region proposals for face detection"):
  with members:
    - "dsfd dual shot face detector"
    - "autofocus efficient multi scale inference"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"

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)