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: "learning optimal and fair decision trees for non discriminative decision making"
  Abstract: In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decision-making (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

Connected through hyperedge HE-1 (induced by source paper "decision trees for decision making under the predict then optimize framework"):
  with members:
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "optimal sparse decision trees"
    - "generalization bounds in the predict then optimize framework"
    - "end to end learning and optimization on graphs"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "task based end to end model learning in stochastic optimization"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-2 (induced by source paper "learning fair rule lists"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "the bouncer problem challenges to remote explainability"
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "faht an adaptive fairness aware decision tree classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "how do i fool you manipulating user trust via misleading black box explanations"

Connected through hyperedge HE-3 (induced by source paper "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-4 (induced by source paper "fairness aware process mining"):
  with members:
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a survey on measuring indirect discrimination in machine learning"
    - "fairness through awareness"

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)