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 transfer across domains for face antispoofing"
  Abstract: A practical face recognition system demands not only high recognition performance but also the capability of detecting spoofing attacks. While emerging approaches to face antispoofing have been proposed in recent years, most of them perform poorly on unseen samples. The generalizability of face antispoofing needs to be significantly improved before it can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.g., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials makes the spoofing attacks quite different. Another reason for the poor generalizability is that limited labeled data are available for training for face antispoofing. We focus on improving the generalizability of convolutional neural network (CNN)-based face antispoofing methods across different kinds of datasets. We propose a deep domain transfer CNN using sparsely unlabeled data from the target domain to learn features that are invariant across domains for face antispoofing. Experiments on five face spoofing datasets show that the proposed method significantly improves the cross-test performance only with a small number of unlabeled samples from the target domain.

Connected through hyperedge HE-1 (induced by source paper "face attribute invertion"):
  with members:
    - "enhance the motion cues for face anti spoofing using cnn lstm architecture"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-2 (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"
    - "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"
    - "face anti spoofing based on color texture analysis"

Connected through hyperedge HE-3 (induced by source paper "defending from adversarial examples with a two stream architecture"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "enhance the motion cues for face anti spoofing using cnn lstm architecture"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning robust 3d face reconstruction and discriminative identity representation"):
  with members:
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"
    - "face alignment in full pose range a 3d total solution"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "dense face alignment"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

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)