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: "hyperparameter optimization of deep neural networks using non probabilistic rbf surrogate model"
  Abstract: Recently, Bayesian optimization has been successfully applied for optimizing hyperparameters of deep neural networks, significantly outperforming the expert-set hyperparameter values. The methods approximate and minimize the validation error as a function of hyperparameter values through probabilistic models like Gaussian processes. However, probabilistic models that require a prior distribution of the errors may be not adequate for approximating very complex error functions of deep neural networks. In this work, we propose to employ radial basis function as the surrogate of the error functions for optimizing both continuous and integer hyperparameters. The proposed non-probabilistic algorithm, called Hyperparameter Optimization using RBF and DYCORS (HORD), searches the surrogate for the most promising hyperparameter values while providing a good balance between exploration and exploitation. Extensive evaluations demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as Spearmint and TPE, both in terms of finding a near optimal solution with fewer expensive function evaluations, and in terms of a final validation error. Further, HORD performs equally well in low- and high-dimensional hyperparameter spaces, and by avoiding expensive covariance computation can also scale to a high number of observations.

Connected through hyperedge HE-1 (induced by source paper "operational calculus for differentiable programming"):
  with members:
    - "associative long short term memory"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a neural algorithm of artistic style"
    - "automatic differentiation in machine learning a survey"
    - "neural programmer interpreters"
    - "the effects of hyperparameters on sgd training of neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "hyperparameter transfer learning through surrogate alignment for efficient deep neural network training"):
  with members:
    - "deep residual learning for image recognition"

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.AI(Artificial Intelligence)