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: "calibration for the computationally identifiable masses"
  Abstract: As algorithms increasingly inform and influence decisions made about individuals, it becomes increasingly important to address concerns that these algorithms might be discriminatory. The output of an algorithm can be discriminatory for many reasons, most notably: (1) the data used to train the algorithm might be biased (in various ways) to favor certain populations over others; (2) the analysis of this training data might inadvertently or maliciously introduce biases that are not borne out in the data. This work focuses on the latter concern. #R##N#We develop and study multicalbration -- a new measure of algorithmic fairness that aims to mitigate concerns about discrimination that is introduced in the process of learning a predictor from data. Multicalibration guarantees accurate (calibrated) predictions for every subpopulation that can be identified within a specified class of computations. We think of the class as being quite rich; in particular, it can contain many overlapping subgroups of a protected group. #R##N#We show that in many settings this strong notion of protection from discrimination is both attainable and aligned with the goal of obtaining accurate predictions. Along the way, we present new algorithms for learning a multicalibrated predictor, study the computational complexity of this task, and draw new connections to computational learning models such as agnostic learning.

Connected through hyperedge HE-1 (induced by source paper "putting fairness principles into practice challenges metrics and improvements"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "delayed impact of fair machine learning"
    - "fairness under composition"
    - "counterfactual fairness in text classification through robustness"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "penalizing unfairness in binary classification"

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"
    - "creating fair models of atherosclerotic cardiovascular disease risk"

Connected through hyperedge HE-3 (induced by source paper "tracking and improving information in the service of fairness"):
  with members:
    - "algorithmic fairness"
    - "50 years of test un fairness lessons for machine learning"
    - "delayed impact of fair machine learning"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the price of fair pca one extra dimension"
    - "access to population level signaling as a source of inequality"

Connected through hyperedge HE-4 (induced by source paper "on the apparent conflict between individual and group fairness"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "eliciting and enforcing subjective individual fairness"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness in machine learning lessons from political philosophy"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-5 (induced by source paper "multi differential fairness auditor for black box classifiers"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "differentially private fair learning"
    - "the frontiers of fairness in machine learning"
    - "fairness through computationally bounded awareness"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "an intersectional definition of fairness"
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-6 (induced by source paper "preference informed fairness"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "individual fairness in sponsored search auctions"
    - "eliciting and enforcing subjective individual fairness"
    - "envy free classification"
    - "online learning with an unknown fairness metric"
    - "fairness through computationally bounded awareness"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-7 (induced by source paper "maximum weighted loss discrepancy"):
  with members:
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-8 (induced by source paper "learning adversarially fair and transferable representations"):
  with members:
    - "mitigating unwanted biases with adversarial learning"
    - "penalizing unfairness in binary classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"
    - "provably fair representations"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "equality of opportunity in supervised learning"
    - "semantic segmentation using adversarial networks"

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)