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 de spoofing anti spoofing via noise modeling"
  Abstract: Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an indivisible unit, and process it holistically, without explicit modeling of the spoofing process. In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification. A CNN architecture with proper constraints and supervisions is proposed to overcome the problem of having no ground truth for the decomposition. We evaluate the proposed method on multiple face anti-spoofing databases. The results show promising improvements due to our spoof noise modeling. Moreover, the estimated spoof noise provides a visualization which helps to understand the added spoof noise by each spoof medium.

Connected through hyperedge HE-1 (induced by source paper "deep frequent spatial temporal learning for face anti spoofing"):
  with members:
    - "specular and diffuse reflection based face liveness detection for mobile devices"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"

Connected through hyperedge HE-2 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-3 (induced by source paper "face anti spoofing by learning polarization cues in a real world scenario"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "paraph presentation attack rejection by analyzing polarization hypotheses"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "casia surf a large scale multi modal benchmark for face anti spoofing"):
  with members:
    - "refineface refinement neural network for high performance face detection"
    - "mis classified vector guided softmax loss for face recognition"
    - "improved selective refinement network for face detection"
    - "ensemble soft margin softmax loss for image classification"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-5 (induced by source paper "attacking cnn based anti spoofing face authentication in the physical domain"):
  with members:
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "synthesizing robust adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-6 (induced by source paper "style transfer applied to face liveness detection with user centered models"):
  with members:
    - "meta anti spoofing learning to learn in face anti spoofing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "how far did we get in face spoofing detection"
    - "a style based generator architecture for generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "learning generalizable and identity discriminative representations for face anti spoofing"):
  with members:
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "pairwise confusion for fine grained visual classification"
    - "perceptual losses for real time style transfer and super resolution"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "learn convolutional neural network for face anti spoofing"

Connected through hyperedge HE-8 (induced by source paper "ropad robust presentation attack detection through unsupervised adversarial invariance"):
  with members:
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "unsupervised adversarial invariance"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "very deep convolutional networks for large scale image recognition"
    - "learn convolutional neural network for face anti spoofing"

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)