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: "low resolution face recognition"
  Abstract: Whilst recent face-recognition (FR) techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at large scales. In this work, we examine systematically this under-studied FR problem, and introduce a novel Complement Super-Resolution and Identity (CSRI) joint deep learning method with a unified end-to-end network architecture. We further construct a new large-scale dataset TinyFace of native unconstrained low-resolution face images from selected public datasets, because none benchmark of this nature exists in the literature. With extensive experiments we show there is a significant gap between the reported FR performances on popular benchmarks and the results on TinyFace, and the advantages of the proposed CSRI over a variety of state-of-the-art FR and super-resolution deep models on solving this largely ignored FR scenario. The TinyFace dataset is released publicly at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "landmark detection in low resolution faces with semi supervised learning"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "spectral normalization for generative adversarial networks"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-2 (induced by source paper "characteristic regularisation for super resolving face images"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "super identity convolutional neural network for face hallucination"
    - "surveillance face recognition challenge"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "cross resolution learning for face recognition"):
  with members:
    - "super identity convolutional neural network for face hallucination"
    - "deep face recognition a survey"
    - "additive margin softmax for face verification"
    - "surveillance face recognition challenge"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

Connected through hyperedge HE-4 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "deep semantic face deblurring"
    - "surveillance face recognition challenge"
    - "face super resolution through wasserstein gans"
    - "s 3 fd single shot scale invariant face detector"

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)