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: "fcos fully convolutional one stage object detection"
  Abstract: We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor-box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training and significantly reduces the training memory footprint. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), our detector FCOS outperforms previous anchor-based one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "depth coefficients for depth completion"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "hierarchical deep stereo matching on high resolution images"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-2 (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-3 (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"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "foveabox beyond anchor based object detector"
    - "pixor real time 3d object detection from point clouds"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "cubeslam monocular 3 d object slam"

Connected through hyperedge HE-7 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "towards end to end text spotting in natural scenes"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"

Connected through hyperedge HE-8 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "disentangling monocular 3d object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "objects as points"

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)