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: "face anti spoofing based on color texture analysis"
  Abstract: Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.

Connected through hyperedge HE-1 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "video action recognition via neural architecture searching"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural architecture search for deep face recognition"
    - "searching for a robust neural architecture in four gpu hours"
    - "customizable architecture search for semantic segmentation"
    - "auto reid searching for a part aware convnet for person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep spatial gradient and temporal depth learning for face anti spoofing"):
  with members:
    - "aurora guard real time face anti spoofing via light reflection"
    - "improving face anti spoofing by 3d virtual synthesis"
    - "exploring hypergraph representation on face anti spoofing beyond 2d attacks"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "biometric face presentation attack detection with multi channel convolutional neural network"):
  with members:
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "lcnn low level feature embedded cnn for salient object detection"
    - "a light cnn for deep face representation with noisy labels"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "domain agnostic feature learning for image and video based face anti spoofing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep transfer across domains for face antispoofing"
    - "facespoof buster a presentation attack detector based on intrinsic image properties and deep learning"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "unified deep supervised domain adaptation and generalization"
    - "deeper broader and artier domain generalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "unseen face presentation attack detection using class specific sparse one class multiple kernel fusion regression"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep anomaly detection for generalized face anti spoofing"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural class specific regression for face verification"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "enhance the motion cues for face anti spoofing using cnn lstm architecture"):
  with members:
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "the treasure beneath convolutional layers cross convolutional layer pooling for image classification"
    - "fully convolutional networks for semantic segmentation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "spoofing and anti spoofing with wax figure faces"):
  with members:
    - "a database for face presentation attack using wax figure faces"
    - "face de spoofing anti spoofing via noise modeling"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-8 (induced by source paper "smartphone multi modal biometric authentication database and evaluation"):
  with members:
    - "mobibits multimodal mobile biometric database"
    - "voxceleb a large scale speaker identification dataset"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "facenet a unified embedding for face recognition and clustering"

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)