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: "men also like shopping reducing gender bias amplification using corpus level constraints"
  Abstract: Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "quantifying and alleviating the language prior problem in visual question answering"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-3 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis"):
  with members:
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "explanation methods in deep learning users values concerns and challenges"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "towards interpretable r cnn by unfolding latent structures"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-4 (induced by source paper "don t judge an object by its context learning to overcome contextual bias"):
  with members:
    - "does object recognition work for everyone"
    - "mitigating gender bias in natural language processing literature review"
    - "repair removing representation bias by dataset resampling"
    - "class balanced loss based on effective number of samples"
    - "laso label set operations networks for multi label few shot learning"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-5 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition"):
  with members:
    - "mitigating gender bias in natural language processing literature review"
    - "huggingface s transformers state of the art natural language processing"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "how to fine tune bert for text classification"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "counterfactual fairness in text classification through robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "machines learn appearance bias in face recognition"):
  with members:
    - "saving face investigating the ethical concerns of facial recognition auditing"
    - "predictive inequity in object detection"
    - "explicit bias discovery in visual question answering models"
    - "women also snowboard overcoming bias in captioning models"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "semantics derived automatically from language corpora contain human like biases"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "revealing neural network bias to non experts through interactive counterfactual examples"):
  with members:
    - "explaining vulnerabilities to adversarial machine learning through visual analytics"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "learning not to learn training deep neural networks with biased data"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

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.AI(Artificial Intelligence)