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: "demographics and discussion influence views on algorithmic fairness"
  Abstract: The field of algorithmic fairness has highlighted ethical questions which may not have purely technical answers. For example, different algorithmic fairness constraints are often impossible to satisfy simultaneously, and choosing between them requires value judgments about which people may disagree. Achieving consensus on algorithmic fairness will be difficult unless we understand why people disagree in the first place. Here we use a series of surveys to investigate how two factors affect disagreement: demographics and discussion. First, we study whether disagreement on algorithmic fairness questions is caused partially by differences in demographic backgrounds. This is a question of interest because computer science is demographically non-representative. If beliefs about algorithmic fairness correlate with demographics, and algorithm designers are demographically non-representative, decisions made about algorithmic fairness may not reflect the will of the population as a whole. We show, using surveys of three separate populations, that there are gender differences in beliefs about algorithmic fairness. For example, women are less likely to favor including gender as a feature in an algorithm which recommends courses to students if doing so would make female students less likely to be recommended science courses. Second, we investigate whether people's views on algorithmic fairness can be changed by discussion and show, using longitudinal surveys of students in two computer science classes, that they can.

Connected through hyperedge HE-1 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "society in the loop programming the algorithmic social contract"
    - "the frontiers of fairness in machine learning"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a reductions approach to fair classification"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-2 (induced by source paper "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"):
  with members:
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "on formalizing fairness in prediction with machine learning"
    - "calibrated fairness in bandits"
    - "the social dilemma of autonomous vehicles"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "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.CY(Computers and Society)