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: "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"
  Abstract: 3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images to render 3D face reconstruction or dense face alignment. However, the shortage of training data with 3D annotations considerably limits performance of those methods. To alleviate this issue, we propose a novel 2D-assisted self-supervised learning (2DASL) method that can effectively use "in-the-wild" 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmarks as additional information, 2DSAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the 2D and 3D landmark self-prediction consistency, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark predictions. Using these four self-supervision schemes, the 2DASL method significantly relieves demands on the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on multiple challenging datasets show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin.

Connected through hyperedge HE-1 (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"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "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 "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"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "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-3 (induced by source paper "learning robust 3d face reconstruction and discriminative identity representation"):
  with members:
    - "deep transfer across domains for face antispoofing"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep transfer across domains for face antispoofing"):
  with members:
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deep domain confusion maximizing for domain invariance"
    - "domain adaptive neural networks for object recognition"
    - "learn convolutional neural network for face anti spoofing"
    - "representation learning a review and new perspectives"

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)