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: "gtapprox surrogate modeling for industrial design"
  Abstract: We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of test problems. In addition, we describe several applications of GTApprox to real engineering problems.

Connected through hyperedge HE-1 (induced by source paper "rare failure prediction via event matching for aerospace applications"):
  with members:
    - "topological data analysis of time series data for b2b customer relationship management"
    - "demand forecasting techniques for build to order lean manufacturing supply chains"
    - "learning ensembles of anomaly detectors on synthetic data"
    - "adaptive hedging under delayed feedback"
    - "aggregating strategies for long term forecasting"
    - "anonymous walk embeddings"
    - "machine learning in appearance based robot self localization"
    - "automatic construction of a recurrent neural network based classifier for vehicle passage detection"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks predicting oil movement in a development unit"):
  with members:
    - "a general spatio temporal clustering based non local formulation for multiscale modeling of compartmentalized reservoirs"
    - "robust locally linear controllable embedding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.MS(Mathematical Software)