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: "use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation"
  Abstract: There is a growing interest in using Generative Adversarial Networks (GANs) to produce image content that is indistinguishable from a real image as judged by a typical person. A number of GAN variants for this purpose have been proposed, however, evaluating GANs is inherently difficult because current methods of measuring the quality of the output do not always mirror what is actually perceived by a human. We propose a novel approach that deploys a brain-computer interface to generate a neural score that closely mirrors the behavioral ground truth measured from participants discerning real from synthetic images. In this paper, we first compare the three most widely used metrics in the literature for evaluating GANs in terms of visual quality compared to human judgments. Second, we propose and demonstrate a novel approach using neural signals and rapid serial visual presentation (RSVP) that directly measures a human perceptual response to facial production quality independent of a behavioral response measurement. Finally we show that our neural score is more consistent with human judgment compared to the conventional metrics we evaluated. We conclude that neural signals have potential application for high quality, rapid evaluation of GANs in the context of visual image synthesis.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a neuro ai interface for evaluating generative adversarial networks"):
  with members:
    - "generative adversarial networks in computer vision a survey and taxonomy"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "openloris object a dataset and benchmark towards lifelong object recognition"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-3 (induced by source paper "synthetic neuroscore using a neuro ai interface for evaluating generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "quick and easy time series generation with established image based gans"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pros and cons of gan evaluation measures"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "neuroscore a brain inspired evaluation metric for generative adversarial networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improved techniques for training gans"
    - "generative moment matching networks"

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)