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: "discovering reliable approximate functional dependencies"
  Abstract: Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or α-approximate top-k dependencies? These are exactly the questions we answer in this paper.   As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.

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"
    - "holoclean holistic data repairs with probabilistic inference"
    - "qfix diagnosing errors through query histories"
    - "finding optimal bayesian networks"
    - "statistical distortion consequences of data cleaning"

Connected through hyperedge HE-2 (induced by source paper "learning functional dependencies with sparse regression"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "a formal framework for probabilistic unclean databases"
    - "holoclean holistic data repairs with probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "discovering reliable correlations in categorical data"):
  with members:
    - "discovering reliable dependencies from data hardness and improved algorithms"
    - "discovering structure in high dimensional data through correlation explanation"

Connected through hyperedge HE-4 (induced by source paper "discovering reliable dependencies from data hardness and improved algorithms"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "opus an efficient admissible algorithm for unordered search"

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)