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: "u phylogeny undirected provenance graph construction in the wild"
  Abstract: Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media. Nonetheless, most recent image phylogeny solutions cannot properly address cases of forged composite images with multiple donors, an area known as multiple parenting phylogeny (MPP). This paper presents a preliminary undirected graph construction solution for MPP, without any strict assumptions. The algorithm is underpinned by robust image representative keypoints and different geometric consistency checks among matching regions in both images to provide regions of interest for direct comparison. The paper introduces a novel technique to geometrically filter the most promising matches as well as to aid in the shared region localization task. The strength of the approach is corroborated by experiments with real-world cases, with and without image distractors (unrelated cases).

Connected through hyperedge HE-1 (induced by source paper "learning transformation aware embeddings for image forensics"):
  with members:
    - "learning rich features for image manipulation detection"
    - "classification is a strong baseline for deep metric learning"
    - "fighting fake news image splice detection via learned self consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "image processing operations identification via convolutional neural network"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"

Connected through hyperedge HE-2 (induced by source paper "face phylogeny tree using basis functions"):
  with members:
    - "some research problems in biometrics the future beckons"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-3 (induced by source paper "needle in a haystack a framework for seeking small objects in big datasets"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "heated up softmax embedding"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "region based image retrieval revisited"

Connected through hyperedge HE-4 (induced by source paper "beyond pixels image provenance analysis leveraging metadata"):
  with members:
    - "image provenance analysis at scale"
    - "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"
    - "a neural algorithm of artistic style"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "image provenance analysis at scale"):
  with members:
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "visual object tracking performance measures revisited"
    - "going deeper with convolutions"

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)