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 "are sampling heuristics necessary in object detectors"):
  Abstract: To alleviate the imbalance between foregrounds and backgrounds, prevalent object detectors to date are always equipped with sampling heuristics, which have been regarded as a necessary component thus far. In this paper, we challenge this paradigm. Our investigation reveals that, with careful training and inference schemes, the well-known RetinaNet could still achieve similar accuracy even without Focal Loss. Inspired by this observation, we propose Sampling-Free mechanism as an alternative to sampling heuristics, which addresses the imbalance from aspects of initialization, loss and inference, thus avoiding laborious hyper-parameters tuning in sampling heuristics. As extensive experimental results will demonstrate, sampling-free mechanism works well for one-stage, two-stage and anchor-free object detectors, with the better performance achieved than sampling-based models. Moreover, it is also effective for instance segmentation. Given the public available implementation \url{https://github.com/ChenJoya/sampling-free}, we sincerely expect our discovery could simplify object detectors training.

Includes members:
  Member "reppoints point set representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "libra r cnn towards balanced learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "region proposal by guided anchoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "imbalance problems in object detection a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "feature selective anchor free module for single shot object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "bottom up object detection by grouping extreme and center points":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")

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)