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: "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
  Abstract: Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes. A splicing detec- tion and localization algorithm usually takes an input image and produces a binary decision indicating whether the input image has been manipulated, and also a segmentation mask that corre- sponds to the spliced region. Most existing splicing detection and localization pipelines suffer from two main shortcomings: 1) they use handcrafted features that are not robust against subsequent processing (e.g., compression), and 2) each stage of the pipeline is usually optimized independently. In this paper we extend the formulation of the underlying splicing problem to consider two input images, a query image and a potential donor image. Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images. We introduce a novel deep convolutional neural network architecture, called Deep Matching and Validation Network (DMVN), which simultaneously localizes and detects image splicing. The proposed approach does not depend on handcrafted features and uses raw input images to create deep learned representations. Furthermore, the DMVN is end-to-end op- timized to produce the probability estimates and the segmentation masks. Our extensive experiments demonstrate that this approach outperforms state-of-the-art splicing detection methods by a large margin in terms of both AUC score and speed.

Connected through hyperedge HE-1 (induced by source paper "recurrent convolutional strategies for face manipulation detection in videos"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "aird adversarial learning framework for image repurposing detection"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "two stream neural networks for tampered face detection"
    - "deep multimodal image repurposing detection"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-2 (induced by source paper "qatm quality aware template matching for deep learning"):
  with members:
    - "neighbourhood consensus networks"
    - "image to gps verification through a bottom up pattern matching network"
    - "heated up softmax embedding"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "learning spread out local feature descriptors"
    - "scnet learning semantic correspondence"
    - "exploiting semantic information and deep matching for optical flow"

Connected through hyperedge HE-3 (induced by source paper "aird adversarial learning framework for image repurposing detection"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "deep multimodal image repurposing detection"
    - "adversarial feature augmentation for unsupervised domain adaptation"
    - "faceposenet making a case for landmark free face alignment"
    - "multimedia semantic integrity assessment using joint embedding of images and text"
    - "csi a hybrid deep model for fake news detection"
    - "do we really need to collect millions of faces for effective face recognition"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning for image forensics deep matching with atrous convolution"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "adversarial learning for semi supervised semantic segmentation"
    - "copy move forgery detection based on convolutional kernel network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep image homography estimation"
    - "fully trainable deep matching"
    - "semantic segmentation using adversarial networks"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-5 (induced by source paper "image to gps verification through a bottom up pattern matching network"):
  with members:
    - "oatm occlusion aware template matching by consensus set maximization"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "selective deep convolutional features for image retrieval"
    - "revisiting im2gps in the deep learning era"
    - "planet photo geolocation with convolutional neural networks"
    - "large scale image retrieval with attentive deep local features"
    - "face recognition using deep multi pose representations"
    - "template matching with deformable diversity similarity"

Connected through hyperedge HE-6 (induced by source paper "deep multimodal image repurposing detection"):
  with members:
    - "multimedia semantic integrity assessment using joint embedding of images and text"
    - "csi a hybrid deep model for fake news detection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "experiments with random projection"
    - "distributed representations of words and phrases and their compositionality"

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)