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: "real time capable micro doppler signature decomposition of walking human limbs"
  Abstract: Unique micro-Doppler signature (μ-D) of a human body motion can be analyzed as the superposition of different body parts μ-D signatures. Extraction of human limbs μ-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate μ-D signatures of a walking human. Furthermore, a novel limbs μ-D signature time independent decomposition feasibility study is presented based on features as μ-D signatures and range profiles also known as micro-Range (μ-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose μ-D signatures of limbs from a walking human signature on real-time basis.

Connected through hyperedge HE-1 (induced by source paper "an adversarial super resolution remedy for radar design trade offs"):
  with members:
    - "medgan medical image translation using gans"
    - "person identification and body mass index a deep learning based study on micro dopplers"
    - "adversarial inpainting of medical image modalities"
    - "retrospective correction of rigid and non rigid mr motion artifacts using gans"
    - "perceptual adversarial networks for image to image transformation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "person identification and body mass index a deep learning based study on micro dopplers"):
  with members:
    - "micro doppler based human robot classification using ensemble and deep learning approaches"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "towards adversarial denoising of radar micro doppler signatures"):
  with members:
    - "medgan medical image translation using gans"
    - "adversarial inpainting of medical image modalities"
    - "cgans with projection discriminator"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "micro doppler based human robot classification using ensemble and deep learning approaches"):
  with members:
    - "adam a method for stochastic optimization"

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)