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: "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
  Abstract: Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper.

Connected through hyperedge HE-1 (induced by source paper "adaptive structural learning of deep belief network for medical examination data and its knowledge extraction by using c4 5"):
  with members:
    - "fine tuning method by using knowledge acquisition from deep belief network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "building high level features using large scale unsupervised learning"
    - "improved use of continuous attributes in c4 5"

Connected through hyperedge HE-2 (induced by source paper "knowledge extracted from recurrent deep belief network for real time deterministic control"):
  with members:
    - "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "fine tuning method by using knowledge acquisition from deep belief network"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "improved use of continuous attributes in c4 5"

Connected through hyperedge HE-3 (induced by source paper "shortening time required for adaptive structural learning method of deep belief network with multi modal data arrangement"):
  with members:
    - "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "imaging time series to improve classification and imputation"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "adaptive learning method of recurrent temporal deep belief network to analyze time series data"):
  with members:
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-5 (induced by source paper "an adaptive learning method of deep belief network by layer generation algorithm"):
  with members:
    - "fine tuning method by using knowledge acquisition from deep belief network"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-6 (induced by source paper "fine tuning method by using knowledge acquisition from deep belief network"):
  with members:
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "fast and accurate deep network learning by exponential linear units elus"

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.NE(Neural and Evolutionary Computing)