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: "counterfactual fairness in text classification through robustness"
  Abstract: In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "Some people are straight" is nontoxic. We offer a metric, counterfactual token fairness (CTF), for measuring this particular form of fairness in text classifiers, and describe its relationship with group fairness. Further, we offer three approaches, blindness, counterfactual augmentation, and counterfactual logit pairing (CLP), for optimizing counterfactual token fairness during training, bridging the robustness and fairness literature. Empirically, we find that blindness and CLP address counterfactual token fairness. The methods do not harm classifier performance, and have varying tradeoffs with group fairness. These approaches, both for measurement and optimization, provide a new path forward for addressing fairness concerns in text classification.

Connected through hyperedge HE-1 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"

Connected through hyperedge HE-2 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "evaluating differentially private machine learning in practice"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gids gan based intrusion detection system for in vehicle network"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "neuzz efficient fuzzing with neural program smoothing"

Connected through hyperedge HE-3 (induced by source paper "data augmentation for personal knowledge graph population"):
  with members:
    - "a neural architecture for person ontology population"
    - "position aware graph neural networks"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fine grained classification of personal data entities"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "context dependent fine grained entity type tagging"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "reducing sentiment bias in language models via counterfactual evaluation"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "certified robustness to adversarial word substitutions"
    - "release strategies and the social impacts of language models"
    - "multi task deep neural networks for natural language understanding"
    - "reducing gender bias in word level language models with a gender equalizing loss function"
    - "transformer xl attentive language models beyond a fixed length context"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "evaluating the underlying gender bias in contextualized word embeddings"

Connected through hyperedge HE-5 (induced by source paper "incorporating priors with feature attribution on text classification"):
  with members:
    - "consistent individualized feature attribution for tree ensembles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "mitigating unwanted biases with adversarial learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "reducing gender bias in abusive language detection"
    - "learning adversarially fair and transferable representations"
    - "did the model understand the question"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-6 (induced by source paper "putting fairness principles into practice challenges metrics and improvements"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "delayed impact of fair machine learning"
    - "fairness under composition"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "penalizing unfairness in binary classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-7 (induced by source paper "counterfactual reasoning for fair clinical risk prediction"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "identifying and correcting label bias in machine learning"
    - "a framework for understanding unintended consequences of machine learning"
    - "adversarial logit pairing"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "causal reasoning for algorithmic fairness"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "the frontiers of fairness in machine learning"

Connected through hyperedge HE-8 (induced by source paper "detecting bias with generative counterfactual face attribute augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model cards for model reporting"
    - "predictive inequity in object detection"
    - "women also snowboard overcoming bias in captioning models"
    - "explaining explanations an overview of interpretability of machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "axiomatic attribution for deep 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.LG(Machine Learning)