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: "causal discrimination discovery through propensity score analysis"
  Abstract: Social discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions' datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "the disparate effects of strategic manipulation"):
  with members:
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "proxy non discrimination in data driven systems"
    - "runaway feedback loops in predictive policing"
    - "price of transparency in strategic machine learning"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-3 (induced by source paper "open the black box data driven explanation of black box decision systems"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "a survey of methods for explaining black box models"
    - "fairness testing testing software for discrimination"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "exposing the probabilistic causal structure of discrimination"
    - "why should i trust you explaining the predictions of any classifier"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "the measure and mismeasure of fairness a critical review of fair machine learning"):
  with members:
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "algorithmic decision making and the cost of fairness"
    - "proxy non discrimination in data driven systems"
    - "exposing the probabilistic causal structure of discrimination"
    - "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)