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: "discovery of band order dependencies"
  Abstract: We enhance dependency-based data cleaning with approximate band conditional order dependencies (abcODs) as a novel type of integrity constraint (IC). Band ODs express the semantics over attributes that are monotonically related with small variations without there being an intrinsic violation of semantics. To make band ODs relevant to real-world applications, we make them less strict to hold approximately with some exceptions and conditionally on subsets of the data with a mix of ascending and descending directions. Formulating ICs manually requires domain expertise, is prone to human errors, and time consuming. Thus, we study the problem of automatic abcOD discovery. #R##N#The naive solution is prohibitively expensive as it considers all possible segmentations of tuples resulting in exponential data complexity. To reduce the search space, we propose an algorithm that utilize the notion of a longest monotonic band (LMB) to identify longest subsequences of tuples that satisfy a band OD. We formulate the abcOD discovery problem as a constraint optimization problem, and devise a dynamic programming algorithm that determines the optimal solution in polynomial time (super-cubic complexity). To further optimize the performance over the large datasets, we adapt the algorithm to consider pieces (contiguous sequences of tuples) in a greedy fashion. This improves the performance by orders-of-magnitude without sacrificing the precision. When bidirectionally is removed to consider unidirectional abcODs, with all ascending or all descending ordering, we show that our pieces-based algorithm is guaranteed to find the optimal solution. We provide an experimental evaluation of our techniques over real-world datasets.

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)