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 deep belief network by layer generation algorithm"
  Abstract: Deep Belief Network (DBN) has a deep architecture that represents multiple features of input patterns hierarchically with the pre-trained Restricted Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its network structure during the learning phase. Our proposed adaptive learning method can discover the optimal number of hidden neurons and weights and/or layers according to the input space. The model is an important method to take account of the computational cost and the model stability. The regularities to hold the sparse structure of network is considerable problem, since the extraction of explicit knowledge from the trained network should be required. In our previous research, we have developed the hybrid method of adaptive structural learning method of RBM and Learning Forgetting method to the trained RBM. In this paper, we propose the adaptive learning method of DBN that can determine the optimal number of layers during the learning. We evaluated our proposed model on some benchmark data sets.

Connected through hyperedge HE-1 (induced by source paper "knowledge extracted from recurrent deep belief network for real time deterministic control"):
  with members:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
    - "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
    - "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-2 (induced by source paper "shortening time required for adaptive structural learning method of deep belief network with multi modal data arrangement"):
  with members:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
    - "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
    - "imaging time series to improve classification and imputation"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (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 restricted boltzmann machine by neuron generation and annihilation 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-4 (induced by source paper "fine tuning method by using knowledge acquisition from deep belief network"):
  with members:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation 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)