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: "aligned and non aligned double jpeg detection using convolutional neural networks"
  Abstract: Abstract   Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64 × 64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.

Connected through hyperedge HE-1 (induced by source paper "harmonic convolutional networks based on discrete cosine transform"):
  with members:
    - "basisconv a method for compressed representation and learning in cnns"
    - "scattering networks for hybrid representation learning"
    - "hybrid task cascade for instance segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "forensic similarity for digital images"):
  with members:
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "reliability map estimation for cnn based camera model attribution"
    - "camera based image forgery localization using convolutional neural networks"
    - "first steps toward camera model identification with convolutional neural networks"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "zooming into face forensics a pixel level analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "detecting photoshopped faces by scripting photoshop"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "detecting photoshopped faces by scripting photoshop"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "video to video synthesis"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "recycle gan unsupervised video retargeting"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "two stream neural networks for tampered face detection"
    - "everybody dance now"

Connected through hyperedge HE-5 (induced by source paper "detecting colorized images via convolutional neural networks toward high accuracy and good generalization"):
  with members:
    - "fake colorized image detection"
    - "first steps toward camera model identification with convolutional neural networks"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "learning representations for automatic colorization"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "colorful image colorization"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-6 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "gabor convolutional networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "a multi branch convolutional neural network for detecting double jpeg compression"
    - "wavelet convolutional neural networks for texture classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-7 (induced by source paper "detecting deepfake forged contents with separable convolutional neural network and image segmentation"):
  with members:
    - "text based editing of talking head video"
    - "learning rich features for image manipulation detection"
    - "mesonet a compact facial video forgery detection network"
    - "capsule forensics using capsule networks to detect forged images and videos"
    - "two stream neural networks for tampered face detection"
    - "a style based generator architecture for generative adversarial networks"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "deep multi scale discriminative networks for double jpeg compression forensics"):
  with members:
    - "patch alignment manifold matting"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "first steps toward camera model identification with convolutional neural networks"
    - "camera identification with deep convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"

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.CR(Cryptography and Security)