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: "structural health monitoring of cantilever beam a case study using bayesian neural network and deep learning"
  Abstract: The advancement of machine learning algorithms has opened a wide scope for vibration-based Structural Health Monitoring (SHM). Vibration-based SHM is based on the fact that damage will alter the dynamic properties, viz., structural response, frequencies, mode shapes, etc. of the structure. The responses measured using sensors, which are high dimensional in nature, can be intelligently analysed using machine learning techniques for damage assessment. Neural networks employing multilayer architectures are expressive models capable of capturing complex relationships between input–output pairs, but do not account for uncertainty in network outputs. A Bayesian Neural Network (BNN) refers to extending standard networks with posterior inference. It is a neural network with a prior distribution on its weights. Deep learning architectures like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are good candidates for representation learning from high-dimensional data. The advantage of using CNN over multilayer neural networks is that they are good feature extractors as well as classifiers, which eliminates the need for generating hand-engineered features. LSTM networks are mainly used for sequence modelling. This paper presents both a Bayesian-multilayer-perceptron and deep-learning-based approach for damage detection and location identification in beam-like structures. Raw frequency response data simulated using finite element analysis is fed as input of the network. As part of this, frequency response was generated for a series of simulations in the cantilever beam involving different damage scenarios (at different locations and different extents). These frequency responses can be studied without any loss of information, as no manual feature engineering is involved. The results obtained from the models are highly encouraging. This case study shows the effectiveness of the above approaches to predict bending rigidity with an acceptable error rate.

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)