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: "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
  Abstract: The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we describe a new method to expose fake face videos generated with neural networks. Our method is based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Our method is tested over benchmarks of eye-blinking detection datasets and also show promising performance on detecting videos generated with DeepFake.

Connected through hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "detecting gan generated fake images using co occurrence matrices"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"

Connected through hyperedge HE-2 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "use of a capsule network to detect fake images and videos"
    - "learning rich features for image manipulation detection"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "exploiting multi domain visual information for fake news detection"
    - "a hierarchical location prediction neural network for twitter user geolocation"
    - "fact checking meets fauxtography verifying claims about images"
    - "false news detection on social media"
    - "aird adversarial learning framework for image repurposing detection"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-4 (induced by source paper "amp authentication of media via provenance"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "detecting gan generated imagery using color cues"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deepfakes a new threat to face recognition assessment and detection"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-6 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "use of a capsule network to detect fake images and videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fsgan subject agnostic face swapping and reenactment"
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "domain adaptation in multi channel autoencoder based features for robust face anti spoofing"
    - "deep pixel wise binary supervision for face presentation attack detection"

Connected through hyperedge HE-8 (induced by source paper "fakecatcher detection of synthetic portrait videos using biological signals"):
  with members:
    - "video to video synthesis"
    - "mesonet a compact facial video forgery detection network"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "ganimation anatomically aware facial animation from a single image"
    - "detection of deep network generated images using disparities in color components"
    - "deepphys video based physiological measurement using convolutional attention networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

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)