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 "adapted center and scale prediction more stable and more accurate"):
  Abstract: Pedestrian detection benefits from deep learning technology and gains rapid development in recent years. Most of detectors follow general object detection frame, i.e. default boxes and two-stage process. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, we propose some adaptations based on a detector, Center and Scale Prediction(CSP). The main contributions of our paper are: (1) We improve the robustness of CSP and make it easier to train. (2) We propose a novel method to predict width, namely compressing width. (3) We achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) We explore some capabilities of Switchable Normalization which are not mentioned in its original paper.

Includes members:
  Member "psc net learning part spatial co occurence for occluded pedestrian detection":
    also connected through other hyperedges:
      (none)
  Member "semantic head enhanced pedestrian detection in a crowd":
    also connected through other hyperedges:
      (none)
  Member "eurocity persons a novel benchmark for person detection in traffic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "foveabox beyond anchor based object detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "high level semantic feature detection a new perspective for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian detection the elephant in the room")
  Member "mask guided attention network for occluded pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "adaptive nms refining pedestrian detection in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)