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 "retinaface single stage dense face localisation in the wild"):
  Abstract: Though tremendous strides have been made in uncontrolled face detection, accurate and efficient face localisation in the wild remains an open challenge. This paper presents a robust single-stage face detector, named RetinaFace, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning. Specifically, We make contributions in the following five aspects: (1) We manually annotate five facial landmarks on the WIDER FACE dataset and observe significant improvement in hard face detection with the assistance of this extra supervision signal. (2) We further add a self-supervised mesh decoder branch for predicting a pixel-wise 3D shape face information in parallel with the existing supervised branches. (3) On the WIDER FACE hard test set, RetinaFace outperforms the state of the art average precision (AP) by 1.1% (achieving AP equal to 91.4%). (4) On the IJB-C test set, RetinaFace enables state of the art methods (ArcFace) to improve their results in face verification (TAR=89.59% for FAR=1e-6). (5) By employing light-weight backbone networks, RetinaFace can run real-time on a single CPU core for a VGA-resolution image. Extra annotations and code have been made available at: this https URL.

Includes members:
  Member "robust and high performance face detector":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refineface refinement neural network for high performance face detection")
  Member "on learning 3d face morphable model from in the wild images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "make a face towards arbitrary high fidelity face manipulation")
  Member "wider face and pedestrian challenge 2018 methods and results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascade retinanet maintaining consistency for single stage object detection")
  Member "improved selective refinement network for face detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refineface refinement neural network for high performance face detection")
  Member "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks")
  Member "generating 3d faces using convolutional mesh autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "dsfd dual shot face detector":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpnet towards minimal face detector")
  Member "face detection using improved faster rcnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refineface refinement neural network for high performance face 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)