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: "fairtest discovering unwarranted associations in data driven applications"
  Abstract: In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice. We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities. We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects. We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.

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 "designing tools for semi automated detection of machine learning biases an interview study"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "fairsight visual analytics for fairness in decision making"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "fairvis visual analytics for discovering intersectional bias in machine learning"
    - "aequitas a bias and fairness audit toolkit"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a provable defense for deep residual networks"
    - "a survey on bias and fairness in machine learning"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "the efficacy of shield under different threat models"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "auditing data provenance in text generation models"):
  with members:
    - "towards demystifying membership inference attacks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "understanding membership inferences on well generalized learning models"
    - "extreme adaptation for personalized neural machine translation"
    - "knock knock who s there membership inference on aggregate location data"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"

Connected through hyperedge HE-5 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "adversarial computation of optimal transport maps"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "approximating optimal transport with linear programs"
    - "automated test generation to detect individual discrimination in ai models"
    - "hunting for discriminatory proxies in linear regression models"
    - "an empirical study of rich subgroup fairness for machine learning"

Connected through hyperedge HE-6 (induced by source paper "algorithmic decision making in avs understanding ethical and technical concerns for smart cities"):
  with members:
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "semantic foggy scene understanding with synthetic data"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "assessing and addressing algorithmic bias but before we get there"
    - "autonomous vehicles for smart and sustainable cities an in depth exploration of privacy and cybersecurity implications"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "fairness testing testing software for discrimination"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-7 (induced by source paper "fat forensics a python toolbox for algorithmic fairness accountability and transparency"):
  with members:
    - "a nutritional label for rankings"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "datasheets for datasets"
    - "defining locality for surrogates in post hoc interpretablity"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "a reductions approach to fair classification"
    - "a unified approach to interpreting model predictions"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-8 (induced by source paper "capuchin causal database repair for algorithmic fairness"):
  with members:
    - "causal reasoning for algorithmic fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "computing optimal repairs for functional dependencies"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised 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.CY(Computers and Society)