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: "couplenet coupling global structure with local parts for object detection"
  Abstract: The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos"):
  with members:
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "region proposal by guided anchoring"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "towards high performance video object detection for mobiles"

Connected through hyperedge HE-2 (induced by source paper "attributed multi relational attention network for fact checking url recommendation"):
  with members:
    - "learning from fact checkers analysis and generation of fact checking language"
    - "neural graph collaborative filtering"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "harmonious attention network for person re identification"
    - "collaborative memory network for recommendation systems"
    - "large scale learnable graph convolutional networks"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "nais neural attentive item similarity model for recommendation"

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 "centernet keypoint triplets for object detection"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "path aggregation network for instance segmentation"
    - "scratchdet training single shot object detectors from scratch"

Connected through hyperedge HE-6 (induced by source paper "bottom up object detection by grouping extreme and center points"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "path aggregation network for instance segmentation"
    - "improving object localization with fitness nms and bounded iou loss"

Connected through hyperedge HE-7 (induced by source paper "residual bi fusion feature pyramid network for accurate single shot object detection"):
  with members:
    - "making convolutional networks shift invariant again"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "augmentation for small object detection"
    - "cornernet detecting objects as paired keypoints"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "yolov3 an incremental improvement"
    - "cfenet an accurate and efficient single shot object detector for autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "feature selective anchor free module for single shot object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "seeing small faces from robust anchor s perspective"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "detnet a backbone network for object detection"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"

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)