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 "pyramidbox high performance detector for finding tiny face"):
  Abstract: With the rapid development of deep convolutional neural network, face detection has made great progress in recent years. WIDER FACE dataset, as a main benchmark, contributes greatly to this area. A large amount of methods have been put forward where PyramidBox designs an effective data augmentation strategy (Data-anchor-sampling) and context-based module for face detector. In this report, we improve each part to further boost the performance, including Balanced-data-anchor-sampling, Dual-PyramidAnchors and Dense Context Module. Specifically, Balanced-data-anchor-sampling obtains more uniform sampling of faces with different sizes. Dual-PyramidAnchors facilitate feature learning by introducing progressive anchor loss. Dense Context Module with dense connection not only enlarges receptive filed, but also passes information efficiently. Integrating these techniques, PyramidBox++ is constructed and achieves state-of-the-art performance in hard set.

Includes members:
  Member "region proposal by guided anchoring":
    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 "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proposal learning for semi supervised object detection")
  Member "dsfd dual shot face detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kpnet towards minimal face detector")
  Member "introvae introspective variational autoencoders for photographic image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "autofocus efficient multi scale inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "metaanchor learning to detect objects with customized anchors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pointins point based instance segmentation")
  Member "seeing small faces from robust anchor s perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "pyramidbox a context assisted single shot face detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "kpnet towards minimal face detector")

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)