human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "survey of face detection on low quality images"
  Abstract: Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.

Connected through hyperedge HE-1 (induced by source paper "when awgn based denoiser meets real noises"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "path restore learning network path selection for image restoration"
    - "real image denoising with feature attention"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-2 (induced by source paper "hard mining loss based convolutional neural network for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "minimum margin loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "loop descriptor local optimal oriented pattern"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "improving face detection performance with 3d rendered synthetic data"):
  with members:
    - "training deep face recognition systems with synthetic data"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "ganimation anatomically aware facial animation from a single image"
    - "sface an efficient network for face detection in large scale variations"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "recent progress of face image synthesis"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"

Question: Please tell me which class the center node 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)