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: "qfix diagnosing errors through query histories"
  Abstract: Data-driven applications rely on the correctness of their data to function properly and effectively. Errors in data can be incredibly costly and disruptive, leading to loss of revenue, incorrect conclusions, and misguided policy decisions. While data cleaning tools can purge datasets of many errors before the data is used, applications and users interacting with the data can introduce new errors. Subsequent valid updates can obscure these errors and propagate them through the dataset causing more discrepancies. Even when some of these discrepancies are discovered, they are often corrected superficially, on a case-by-case basis, further obscuring the true underlying cause, and making detection of the remaining errors harder. In this paper, we propose QFix, a framework that derives explanations and repairs for discrepancies in relational data, by analyzing the effect of queries that operated on the data and identifying potential mistakes in those queries. QFix is flexible, handling scenarios where only a subset of the true discrepancies is known, and robust to different types of update workloads. We make four important contributions: (a) we formalize the problem of diagnosing the causes of data errors based on the queries that operated on and introduced errors to a dataset; (b) we develop exact methods for deriving diagnoses and fixes for identified errors using state-of-the-art tools; (c) we present several optimization techniques that improve our basic approach without compromising accuracy, and (d) we leverage a tradeoff between accuracy and performance to scale diagnosis to large datasets and query logs, while achieving near-optimal results. We demonstrate the effectiveness of QFix through extensive evaluation over benchmark and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "detecting data errors with statistical constraints"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "discovering reliable approximate functional dependencies"
    - "holoclean holistic data repairs with probabilistic inference"
    - "finding optimal bayesian networks"
    - "statistical distortion consequences of data cleaning"

Connected through hyperedge HE-2 (induced by source paper "debugging machine learning pipelines"):
  with members:
    - "macrobase prioritizing attention in fast data"
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "programing implementation of the quine mccluskey method for minimization of boolean expression"

Connected through hyperedge HE-3 (induced by source paper "explaining aggregates for exploratory analytics"):
  with members:
    - "foresight recommending visual insights"
    - "axiomatic attribution for deep networks"
    - "database learning toward a database that becomes smarter every time"
    - "macrobase prioritizing attention in fast data"
    - "macrobase analytic monitoring for the internet of things"
    - "why should i trust you explaining the predictions of any classifier"
    - "perfxplain debugging mapreduce job performance"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "fairness testing testing software for discrimination"):
  with members:
    - "fairness in learning classic and contextual bandits"
    - "iterative orthogonal feature projection for diagnosing bias in black box models"
    - "fairtest discovering unwarranted associations in data driven applications"
    - "discrimination in online ad delivery"
    - "fairness through awareness"
    - "the complexity of causality and responsibility for query answers and non answers"

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.DB(Databases)