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 "abc of order dependencies"):
  Abstract: We enhance constrained-based data quality with approximate band conditional order dependencies (abcODs). Band ODs model the semantics of attributes that are monotonically related with small variations without there being an intrinsic violation of semantics. The class of abcODs generalizes band ODs to make them more relevant to real-world applications by relaxing them to hold approximately (abODs) with some exceptions and conditionally (bcODs) on subsets of the data. We study the problem of automatic dependency discovery over a hierarchy of abcODs. First, we propose a more efficient algorithm to discover abODs than in recent prior work. The algorithm is based on a new optimization to compute a longest monotonic band (longest subsequence of tuples that satisfy a band OD) through dynamic programming by decreasing the runtime from O(n^2) to O(n \log n) time. We then illustrate that while the discovery of bcODs is relatively straightforward, there exist codependencies between approximation and conditioning that make the problem of abcOD discovery challenging. The naive solution is prohibitively expensive as it considers all possible segmentations of tuples resulting in exponential time complexity. To reduce the search space, we devise a dynamic programming algorithm for abcOD discovery that determines the optimal solution in O(n^3 \log n) complexity. To further optimize the performance, we adapt the algorithm to cheaply identify consecutive tuples that are guaranteed to belong to the same band--this improves the performance significantly in practice without losing optimality. While unidirectional abcODs are most common in practice, for generality we extend our algorithms with both ascending and descending orders to discover bidirectional abcODs. Finally, we perform a thorough experimental evaluation of our techniques over real-world and synthetic datasets.

Includes members:
  Member "distributed dependency discovery":
    also connected through other hyperedges:
      (none)
  Member "effective and complete discovery of order dependencies via set based axiomatization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data invariants on trust in data driven systems")
  Member "fundamentals of order dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovery of band order dependencies")

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)