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: "foveabox beyond anchor based object detector"
  Abstract: We present FoveaBox, an accurate, flexible and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize the predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations for each input image. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance of 42.1 AP on the standard COCO detection benchmark. Specially for the objects with arbitrary aspect ratios, FoveaBox brings in significant improvement compared to the anchor-based detectors. More surprisingly, when it is challenged by the stretched testing images, FoveaBox shows great robustness and generalization ability to the changed distribution of bounding box shapes. The code will be made publicly available.

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"
    - "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"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "adapted center and scale prediction more stable and more accurate"):
  with members:
    - "psc net learning part spatial co occurence for occluded pedestrian detection"
    - "semantic head enhanced pedestrian detection in a crowd"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-3 (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"
    - "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"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-4 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder"):
  with members:
    - "object detection based on region decomposition and assembly"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet lite efficient keypoint based object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "self training with noisy student improves imagenet classification"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-5 (induced by source paper "measuring the utilization of public open spaces by deep learning a benchmark study at the detroit riverfront"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fcos fully convolutional one stage object detection"
    - "object detection with deep learning a review"
    - "detecting and recognizing human object interactions"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "multiple human parsing in the wild"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (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"
    - "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"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-7 (induced by source paper "mixture model based bounding box density estimation for object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "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"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-8 (induced by source paper "learning spatial fusion for single shot object detection"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "adaptively connected neural networks"
    - "bag of freebies for training object detection neural networks"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks 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)