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: "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"
  Abstract: In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network -- and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.

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 "representation learning with statistical independence to mitigate bias"):
  with members:
    - "repair removing representation bias by dataset resampling"
    - "adversarial invariant feature learning with accuracy constraint for domain generalization"
    - "mimetics towards understanding human actions out of context"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "on the global optima of kernelized adversarial representation learning"
    - "why can t i dance in the mall learning to mitigate scene bias in action recognition"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-3 (induced by source paper "measuring social biases in grounded vision and language embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "assessing social and intersectional biases in contextualized word representations"
    - "large scale pretraining for visual dialog a simple state of the art baseline"
    - "iterative answer prediction with pointer augmented multimodal transformers for textvqa"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-5 (induced by source paper "unlearn dataset bias in natural language inference by fitting the residual"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "selection bias explorations and debias methods for natural language sentence matching datasets"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "paws paraphrase adversaries from word scrambling"
    - "theoretically principled trade off between robustness and accuracy"
    - "learning robust representations by projecting superficial statistics out"

Connected through hyperedge HE-6 (induced by source paper "learning de biased representations with biased representations"):
  with members:
    - "similarity of neural network representations revisited"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "sunny and dark outside improving answer consistency in vqa through entailed question generation"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "only time can tell discovering temporal data for temporal modeling"
    - "repair removing representation bias by dataset resampling"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"

Connected through hyperedge HE-7 (induced by source paper "towards fairness in visual recognition effective strategies for bias mitigation"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "on fairness and calibration"
    - "what actions are needed for understanding human actions in videos"
    - "on formalizing fairness in prediction with machine learning"

Connected through hyperedge HE-8 (induced by source paper "maximal adversarial perturbations for obfuscation hiding certain attributes while preserving rest"):
  with members:
    - "distributed generation of privacy preserving data with user customization"
    - "generating user friendly explanations for loan denials using gans"
    - "fair lending needs explainable models for responsible recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "privacy protective gan for face de identification"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "synthesizing robust adversarial examples"

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)