human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "bounding box regression with uncertainty for accurate object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "cirl controllable imitative reinforcement learning for vision based self driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal end to end autonomous driving")
  Member "seeing small faces from robust anchor s perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "m2det a single shot object detector based on multi level feature pyramid network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder")
  Member "detnet a backbone network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "ring loss convex feature normalization for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments")
  Member "sface an efficient network for face detection in large scale variations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pyramidbox high performance detector for finding tiny face")

Question: Please tell me which class the source paper 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)