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: "measurement and fairness"
  Abstract: We introduce the language of measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. Computational systems often involve unobservable theoretical constructs, such as "creditworthiness," "teacher quality," or "risk to society," that cannot be measured directly and must instead be inferred from observable properties thought to be related to them---i.e., operationalized via a measurement model. This process introduces the potential for mismatch between the theoretical understanding of the construct purported to be measured and its operationalization. Indeed, we argue that many of the harms discussed in the literature on fairness in computational systems are direct results of such mismatches. Further complicating these discussions is the fact that fairness itself is an unobservable theoretical construct. Moreover, it is an essentially contested construct---i.e., it has many different theoretical understandings depending on the context. We argue that this contestedness underlies recent debates about fairness definitions: disagreements that appear to be about contradictory operationalizations are, in fact, disagreements about different theoretical understandings of the construct itself. By introducing the language of measurement modeling, we provide the computer science community with a process for making explicit and testing assumptions about unobservable theoretical constructs, thereby making it easier to identify, characterize, and even mitigate fairness-related harms.

Connected through hyperedge HE-1 (induced by source paper "a hierarchy of limitations in machine learning"):
  with members:
    - "case study predictive fairness to reduce misdemeanor recidivism through social service interventions"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "the human body is a black box supporting clinical decision making with deep learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "an open source automl benchmark"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "roles for computing in social change"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"

Connected through hyperedge HE-2 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an intersectional definition of fairness"

Connected through hyperedge HE-3 (induced by source paper "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"):
  with members:
    - "about ml annotation and benchmarking on understanding and transparency of machine learning lifecycles"
    - "ores lowering barriers with participatory machine learning in wikipedia"
    - "decision provenance harnessing data flow for accountable systems"
    - "towards traceability in data ecosystems using a bill of materials model"
    - "datasheets for datasets"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "multilingual twitter sentiment classification the role of human annotators"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-4 (induced by source paper "towards a critical race methodology in algorithmic fairness"):
  with members:
    - "model cards for model reporting"
    - "racial categories in machine learning"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

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)