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: "closed form variances for variational auto encoders"
  Abstract: We propose a reformulation of Variational Auto-Encoders eliminating half of the network outputs (the variances) in a deep network setting. While it is well known that the posterior is in general intractable, we show that the variances of Gaussian posteriors and likelihoods may be solved in closed form, producing improved variational lower bounds over their learned counterparts in experiments. The closed forms reduce to remarkably simple expressions -- in particular, one optimal choice for the posterior variance is simply the identity matrix. We arrive at these conclusions by analyzing the variational lower bound objective irrespective of any particular network architecture, deriving its partial derivatives and closed form solutions for all parameters but the posterior means. In deriving the closed form likelihood variance, we show that the objective is underdetermined, which we resolve by constraining the presumed information content of the data examples. Any of these modifications may be applied to simplify, and perhaps improve, any Variational Auto-Encoder.

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)