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: "creating fair models of atherosclerotic cardiovascular disease risk"
  Abstract: Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance.

Connected through hyperedge HE-1 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-2 (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"
    - "counterfactual fairness in text classification through robustness"
    - "the frontiers of fairness in machine 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.LG(Machine Learning)