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: "learning generalizable and identity discriminative representations for face anti spoofing"
  Abstract: Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems. Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of unseen scenes. In this paper, we try to boost the generalizability and applicability of these methods by designing a CNN model with two major novelties. First, we propose a simple yet effective Total Pairwise Confusion (TPC) loss for CNN training, which enhances the generalizability of the learned Presentation Attack (PA) representations. Secondly, we incorporate a Fast Domain Adaptation (FDA) component into the CNN model to alleviate negative effects brought by domain changes. Besides, our proposed model, which is named Generalizable Face Authentication CNN (GFA-CNN), works in a multi-task manner, performing face anti-spoofing and face recognition simultaneously. Experimental results show that GFA-CNN outperforms previous face anti-spoofing approaches and also well preserves the identity information of input face images.

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"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "face de spoofing anti spoofing via noise modeling"
    - "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 "face attribute invertion"):
  with members:
    - "deep transfer across domains for face antispoofing"
    - "enhance the motion cues for face anti spoofing using cnn lstm architecture"
    - "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-3 (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 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-4 (induced by source paper "defending from adversarial examples with a two stream architecture"):
  with members:
    - "deep transfer across domains for face antispoofing"
    - "one pixel attack for fooling deep neural networks"
    - "enhance the motion cues for face anti spoofing using cnn lstm architecture"
    - "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-5 (induced by source paper "learning robust 3d face reconstruction and discriminative identity representation"):
  with members:
    - "deep transfer across domains for face antispoofing"
    - "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)