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: "fake colorized image detection"
  Abstract: Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.

Connected through hyperedge HE-1 (induced by source paper "fake generated painting detection via frequency analysis"):
  with members:
    - "face x ray for more general face forgery detection"
    - "attention aware multi stroke style transfer"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "distinguishing computer generated graphics from natural images based on sensor pattern noise and deep learning"
    - "do gans leave artificial fingerprints"
    - "detection of deep network generated images using disparities in color components"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "detecting colorized images via convolutional neural networks toward high accuracy and good generalization"):
  with members:
    - "first steps toward camera model identification with convolutional neural networks"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "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"

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.MM(Multimedia)