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: "runaway feedback loops in predictive policing"
  Abstract: Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. #R##N#In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "fairness in learning based sequential decision algorithms a survey"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair contextual multi armed bandits theory and experiments"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "a survey on bias and fairness in machine learning"
    - "fairness with dynamics"
    - "group retention when using machine learning in sequential decision making the interplay between user dynamics and fairness"
    - "combinatorial sleeping bandits with fairness constraints"

Connected through hyperedge HE-3 (induced by source paper "balancing competing objectives with noisy data score based classifiers for welfare aware machine learning"):
  with members:
    - "a longitudinal analysis of youtube s promotion of conspiracy videos"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "fairness and utilization in allocating resources with uncertain demand"
    - "fair classification and social welfare"
    - "noise tolerant fair classification"
    - "delayed impact of fair machine learning"
    - "welfare and distributional impacts of fair classification"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"

Connected through hyperedge HE-4 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "adversarial attacks on copyright detection systems"
    - "explaining explanations in ai"
    - "delayed impact of fair machine learning"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poisoning attacks to graph based recommender systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fighting offensive language on social media with unsupervised text style transfer"

Connected through hyperedge HE-5 (induced by source paper "closing the ai accountability gap defining an end to end framework for internal algorithmic auditing"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "diversity in faces"
    - "model cards for model reporting"
    - "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"
    - "the global landscape of ai ethics guidelines"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"

Connected through hyperedge HE-6 (induced by source paper "steps towards value aligned systems"):
  with members:
    - "mimetic vs anchored value alignment in artificial intelligence"
    - "semantics derived automatically from language corpora contain human like biases"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "whose tweets are surveilled for the police an audit of social media monitoring tool via log files"):
  with members:
    - "on the im possibility of fairness"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"

Connected through hyperedge HE-8 (induced by source paper "a framework for understanding unintended consequences of machine learning"):
  with members:
    - "algorithmic fairness"
    - "model cards for model reporting"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "a convex framework for fair regression"
    - "on formalizing fairness in prediction with machine learning"
    - "algorithmic decision making and the cost of fairness"

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)