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: "feature selective anchor free module for single shot object detection"
  Abstract: We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO.

Connected through hyperedge HE-1 (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"
    - "deeper and wider siamese networks for real time visual tracking"

Connected through hyperedge HE-2 (induced by source paper "har net joint learning of hybrid attention for single stage object detection"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "sharp attention network via adaptive sampling for person re identification"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement"):
  with members:
    - "baod budget aware object detection"
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "region proposal by guided anchoring"
    - "revisiting feature alignment for one stage object detection"
    - "min entropy latent model for weakly supervised object detection"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "rethinking imagenet pre training"

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:
    - "foveabox beyond anchor based object detector"
    - "object detection based on region decomposition and assembly"
    - "sinet a scale insensitive convolutional neural network for fast vehicle 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 "universal rcnn universal object detector via transferable graph r cnn"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "frage frequency agnostic word representation"
    - "detnet a backbone network for object detection"
    - "hybrid knowledge routed modules for large scale object detection"

Connected through hyperedge HE-6 (induced by source paper "extended feature pyramid network for small object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "image super resolution by neural texture transfer"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "task driven super resolution object detection in low resolution images"

Connected through hyperedge HE-7 (induced by source paper "object detection on single monocular images through canonical correlation analysis"):
  with members:
    - "a survey of deep learning based object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "high quality monocular depth estimation via transfer learning"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "focal loss for dense object detection"
    - "relation networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "matrixnets a new scale and aspect ratio aware architecture for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "cornernet lite efficient keypoint based object detection"
    - "freeanchor learning to match anchors for visual object detection"
    - "cornernet detecting objects as paired keypoints"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object 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)