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: "gender from iris or gender from mascara"
  Abstract: Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "achieving robustness in the wild via adversarial mixing with disentangled representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "do imagenet classifiers generalize to imagenet"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"

Connected through hyperedge HE-3 (induced by source paper "gender classification from iris texture images using a new set of binary statistical image features"):
  with members:
    - "gender and ethnicity classification of iris images using deep class encoder"

Connected through hyperedge HE-4 (induced by source paper "deep learning predicts hip fracture using confounding patient and healthcare variables"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "fast and accurate classification of echocardiograms using deep learning"
    - "semantics derived automatically from language corpora contain human like biases"
    - "detecting hip fractures with radiologist level performance using deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "predicting gender from iris texture may be harder than it seems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "predicting gender and race from near infrared iris and periocular images"
    - "gender and ethnicity classification of iris images using deep class encoder"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "11k hands gender recognition and biometric identification using a large dataset of hand images"):
  with members:
    - "which facial expressions can reveal your gender a study with 3d faces"
    - "vegac visual saliency based age gender and facial expression classification using convolutional neural networks"
    - "can we boost the power of the viola jones face detector using pre processing an empirical study"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"
    - "factors of transferability for a generic convnet representation"

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)