human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "explaining wrong queries using small examples"):
  Abstract: For testing the correctness of SQL queries, e.g., evaluating student submissions in a database course, a standard practice is to execute the query in question on some test database instance and compare its result with that of the correct query. Given two queries $Q_1$ and $Q_2$, we say that a database instance $D$ is a counterexample (for $Q_1$ and $Q_2$) if $Q_1(D)$ differs from $Q_2(D)$; such a counterexample can serve as an explanation of why $Q_1$ and $Q_2$ are not equivalent. While the test database instance may serve as a counterexample, it may be too large or complex to read and understand where the inequivalence comes from. Therefore, in this paper, given a known counterexample $D$ for $Q_1$ and $Q_2$, we aim to find the smallest counterexample $D' \subseteq D$ where $Q_1(D') \neq Q_2(D')$. The problem in general is NP-hard. We give a suite of algorithms for finding the smallest counterexample for different classes of queries, some more tractable than others. We also present an efficient provenance-based algorithm for SPJUD queries that uses a constraint solver, and extend it to more complex queries with aggregation, group-by, and nested queries. We perform extensive experiments indicating the effectiveness and scalability of our solution on student queries from an undergraduate database course and on queries from the TPC-H benchmark. We also report a user study from the course where we deployed our tool to help students with an assignment on relational algebra.

Includes members:
  Member "smoke fine grained lineage at interactive speed":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vamsa tracking provenance in data science scripts")
  Member "semi supervised verified feedback generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for bug localization in student programs")
  Member "data generation for testing and grading sql queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edit based grading of sql queries")
  Member "on the limitations of provenance for queries with difference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provenance analysis for logic and games")
  Member "provenance for aggregate queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "priu a provenance based approach for incrementally updating regression models")
  Member "faster query answering in probabilistic databases using read once functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provenance circuits for trees and treelike instances extended version")

Question: Please tell me which class the source paper 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)