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: "dropout strikes back improved uncertainty estimation via diversity sampled implicit ensembles"
  Abstract: Modern machine learning models usually do not extrapolate well, i.e., they often have high prediction errors in the regions of sample space lying far from the training data. In high dimensional spaces detecting out-of-distribution points becomes a non-trivial problem. Thus, uncertainty estimation for model predictions becomes crucial for the successful application of machine learning models in many applications. In this work, we show that increasing the diversity of realizations sampled from a neural network with dropout helps to improve the quality of uncertainty estimation. In a series of experiments on simulated and real-world data, we demonstrate that diversification via determinantal point processes-based sampling allows achieving state-of-the-art results in uncertainty estimation for regression and classification tasks. Importantly, our approach does not require any modification to the models or training procedures, allowing for straightforward application to any deep learning model with dropout layers.

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)