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: "image provenance analysis at scale"
  Abstract: Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation ( e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.

Connected through hyperedge HE-1 (induced by source paper "generate segment and replace towards generic manipulation segmentation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "two stream neural networks for tampered face detection"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "beyond pixels image provenance analysis leveraging metadata"):
  with members:
    - "fighting fake news image splice detection via learned self consistency"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "anti forensics of camera identification and the triangle test by improved fingerprint copy attack"
    - "provenance filtering for multimedia phylogeny"
    - "u phylogeny undirected provenance graph construction in the wild"
    - "a neural algorithm of artistic style"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "the ps battles dataset an image collection for image manipulation detection"):
  with members:
    - "densely connected convolutional 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)