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: "a performance evaluation of convolutional neural networks for face anti spoofing"
  Abstract: In the current era, biometric based access control is becoming more popular due to its simplicity and ease to use by the users. It reduces the manual work of identity recognition and facilitates the automatic processing. The face is one of the most important biometric visual information that can be easily captured without user cooperation in an uncontrolled environment. Precise detection of spoofed faces should be on the high priority to make face based identity recognition and access control robust against possible attacks. The recently evolved Convolutional Neural Network (CNN) based deep learning technique has proven as one of the excellent method to deal with the visual information very effectively. The CNN learns the hierarchical features at intermediate layers automatically from the data. Several CNN based methods such as Inception and ResNet have shown outstanding performance for image classification problem. This paper does a performance evaluation of CNNs for face anti-spoofing. The Inception and ResNet CNN architectures are used in this study. The results are computed over benchmark MSU Mobile Face Spoofing Database. The experiments are done by considering the different aspects such as the depth of the model, random weight initialization vs weight transfer, fine tuning vs training from scratch and different learning rate. The favorable results are obtained using these CNN architectures for face anti-spoofing in different settings.

Connected through hyperedge HE-1 (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"
    - "deep residual learning for image recognition"
    - "face anti spoofing based on color texture analysis"

Connected through hyperedge HE-2 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "3 d deep learning approach for remote sensing image classification"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "diffgrad an optimization method for convolutional neural networks"):
  with members:
    - "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"
    - "on the convergence of adam and beyond"
    - "predictive local smoothness for stochastic gradient methods"
    - "path aggregation network for instance segmentation"
    - "signsgd compressed optimisation for non convex problems"
    - "nostalgic adam weighing more of the past gradients when designing the adaptive learning rate"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "lstm a search space odyssey"

Connected through hyperedge HE-4 (induced by source paper "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"):
  with members:
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "complex valued neural networks with non parametric activation functions"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "dfunet convolutional neural networks for diabetic foot ulcer classification"
    - "visualizing the loss landscape of neural nets"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "meta anti spoofing learning to learn in face anti spoofing"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "rethink and redesign meta learning"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "hard mining loss based convolutional neural network for face recognition"
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "arcface additive angular margin loss for deep face recognition"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

Connected through hyperedge HE-7 (induced by source paper "improving face anti spoofing by 3d virtual synthesis"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "face synthesis for eyeglass robust face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "learning camera viewpoint using cnn to improve 3d body pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "synthesizing training images for boosting human 3d pose estimation"

Connected through hyperedge HE-8 (induced by source paper "learning meta model for zero and few shot face anti spoofing"):
  with members:
    - "deep tree learning for zero shot face anti spoofing"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "rethink and redesign meta learning"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"

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)