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: "freeanchor learning to match anchors for visual object detection"
  Abstract: Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.

Connected through hyperedge HE-1 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking"):
  with members:
    - "gridmask data augmentation"
    - "reppoints point set representation for object detection"
    - "res2net a new multi scale backbone architecture"
    - "foveabox beyond anchor based object detector"
    - "making convolutional networks shift invariant again"
    - "region proposal by guided anchoring"
    - "mixconv mixed depthwise convolutional kernels"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"

Connected through hyperedge HE-2 (induced by source paper "matrixnets a new scale and aspect ratio aware architecture for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "cornernet lite efficient keypoint based object detection"
    - "cornernet detecting objects as paired keypoints"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (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-4 (induced by source paper "mixture model based bounding box density estimation for object detection"):
  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"
    - "revisiting feature alignment for one stage object detection"
    - "towards accurate one stage object detection with ap loss"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "cspnet a new backbone that can enhance learning capability of cnn"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "thundernet towards real time generic object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "objects as points"
    - "hardnet a low memory traffic network"
    - "cornernet lite efficient keypoint based object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "multiple anchor learning for visual object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "region proposal by guided anchoring"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "improving object detection with inverted attention"
    - "c mil continuation multiple instance learning for weakly supervised object detection"

Connected through hyperedge HE-7 (induced by source paper "soft anchor point object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "information competing process for learning diversified representations"):
  with members:
    - "attribute guided unpaired image to image translation with semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "towards visual feature translation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "neural expectation maximization"
    - "opening the black box of deep neural networks via information"

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)