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: "augmentor an image augmentation library for machine learning"
  Abstract: The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.

Connected through hyperedge HE-1 (induced by source paper "imperfect imaganation implications of gans exacerbating biases on facial data augmentation and snapchat selfie lenses"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "mesonet a compact facial video forgery detection network"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "a style based generator architecture for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"
    - "deepfakes a new threat to face recognition assessment and detection"

Connected through hyperedge HE-2 (induced by source paper "deep convolutions for in depth automated rock typing"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "adapting convolutional neural networks for geographical domain shift"
    - "improved regularization of convolutional neural networks with cutout"
    - "understanding deep learning requires rethinking generalization"
    - "why should i trust you explaining the predictions of any classifier"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning dense wide baseline stereo matching for people"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "stereodrnet dilated residual stereo net"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "fkimnet a finger dorsal image matching network comparing component major minor and nail matching with holistic finger dorsal matching"):
  with members:
    - "fdsnet finger dorsal image spoof detection network using light field camera"
    - "ubsegnet unified biometric region of interest segmentation network"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-6 (induced by source paper "exploring correlations in multiple facial attributes through graph attention network"):
  with members:
    - "pseudo task augmentation from deep multitask learning to intratask sharing and back"
    - "an overview of multi task learning in deep neural networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "cross stitch networks for multi task learning"
    - "imagenet pre trained models with batch normalization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "semi supervised classification with graph convolutional networks"
    - "trace norm regularised deep multi task learning"

Connected through hyperedge HE-7 (induced by source paper "skin lesions classification using convolutional neural networks in clinical images"):
  with members:
    - "do better imagenet models transfer better"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "autoaugment learning augmentation policies from data"
    - "knowledge transfer for melanoma screening with deep learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "how transferable are features in deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "fdfnet a secure cancelable deep finger dorsal template generation network secured via bio hashing"):
  with members:
    - "local binary convolutional neural 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)