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: "exposing the probabilistic causal structure of discrimination"
  Abstract: Discrimination discovery from data is an important data mining task, whose goal is to identify patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities. While any legally valid proof of discrimination requires evidence of causality, the state-of-the-art methods are essentially correlation based, albeit, as it is well known, correlation does not imply causation. In this paper, we take a principled causal approach to discrimination detection following Suppes’ probabilistic causation theory. In particular, we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data. The result is a type of constrained Bayesian network, which we dub Suppes-Bayes causal network (SBCN). Next, we develop a toolkit of methods based on random walks on top of the SBCN, addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism. Our experiments on real-world datasets confirm the inferential power of our approach in all these different tasks.

Connected through hyperedge HE-1 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-2 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"

Connected through hyperedge HE-3 (induced by source paper "fairness in algorithmic decision making an excursion through the lens of causality"):
  with members:
    - "fairness under composition"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "causal reasoning for algorithmic fairness"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "big data data science and civil rights"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "a causal framework for discovering and removing direct and indirect discrimination"

Connected through hyperedge HE-4 (induced by source paper "causal inference for social discrimination reasoning"):
  with members:
    - "the sensitivity of counterfactual fairness to unmeasured confounding"
    - "causal reasoning for algorithmic fairness"
    - "achieving non discrimination in data release"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-5 (induced by source paper "learning bayesian networks from demographic and health survey data"):
  with members:
    - "evaluating structure learning algorithms with a balanced scoring function"
    - "d separation from theorems to algorithms"
    - "generating connected acyclic digraphs uniformly at random"

Connected through hyperedge HE-6 (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"
    - "causal discrimination discovery through propensity score analysis"
    - "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-7 (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"
    - "causal discrimination discovery through propensity score analysis"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-8 (induced by source paper "fairness preserving text summarzation"):
  with members:
    - "fair clustering through fairlets"
    - "a survey on neural network based summarization methods"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "fa ir a fair top k ranking algorithm"
    - "how to be fair and diverse"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "budgeted influence maximization for multiple products"
    - "lexrank graph based lexical centrality as salience in text summarization"

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.DB(Databases)