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 recognition via centralized coordinate learning"
  Abstract: Owe to the rapid development of deep neural network (DNN) techniques and the emergence of large scale face databases, face recognition has achieved a great success in recent years. During the training process of DNN, the face features and classification vectors to be learned will interact with each other, while the distribution of face features will largely affect the convergence status of network and the face similarity computing in test stage. In this work, we formulate jointly the learning of face features and classification vectors, and propose a simple yet effective centralized coordinate learning (CCL) method, which enforces the features to be dispersedly spanned in the coordinate space while ensuring the classification vectors to lie on a hypersphere. An adaptive angular margin is further proposed to enhance the discrimination capability of face features. Extensive experiments are conducted on six face benchmarks, including those have large age gap and hard negative samples. Trained only on the small-scale CASIA Webface dataset with 460K face images from about 10K subjects, our CCL model demonstrates high effectiveness and generality, showing consistently competitive performance across all the six benchmark databases.

Connected through hyperedge HE-1 (induced by source paper "rethinking softmax cross entropy loss for adversarial robustness"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-2 (induced by source paper "face recognition a novel multi level taxonomy based survey"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "ring loss convex feature normalization for face recognition"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "nonlinear 3d face morphable model"
    - "a novel channel pruning method for deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "arcface additive angular margin loss for deep face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "rethinking feature distribution for loss functions in image classification"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "challenging images for minds and machines"):
  with members:
    - "densepose dense human pose estimation in the wild"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "east an efficient and accurate scene text detector"
    - "prototypical networks for few shot learning"
    - "failures of gradient based deep learning"
    - "learning visual reasoning without strong priors"
    - "film visual reasoning with a general conditioning layer"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-5 (induced by source paper "normalization before shaking toward learning symmetrically distributed representation without margin in speech emotion recognition"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "arcface additive angular margin loss for deep face recognition"
    - "shakedrop regularization for deep residual learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "convolutional sequence to sequence learning"
    - "shake shake regularization"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"

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)