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: "on the uniform convergence of consistent confidence measures"
  Abstract: Many classification algorithms produce confidence measures in the form of conditional probability of labels given the features of the target instance. It is desirable to be make these confidence measures calibrated or consistent, in the sense that they correctly capture the belief of the algorithm in the label output. For instance, if the algorithm outputs a label with confidence measure $p$ for $n$ times, then the output label should be correct approximately $np$ times overall. Calibrated confidence measures lead to higher interpretability by humans and computers and enable downstream analysis or processing. In this paper, we formally characterize the consistency of confidence measures and prove a PAC-style uniform convergence result for the consistency of confidence measures. We show that finite VC-dimension is sufficient for guaranteeing the consistency of confidence measures produced by empirically consistent classifiers. Our result also implies that we can calibrate confidence measures produced by any existing algorithms with monotonic functions, and still get the same generalization guarantee on consistency.

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.LG(Machine Learning)