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: "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
  Abstract: Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Such architecture is well known to represent higher learning capability compared with some conventional models if the best set of parameters in the optimal network structure is found. We have been developing the adaptive learning method that can discover the optimal network structure in Deep Belief Network (DBN). The learning method can construct the network structure with the optimal number of hidden neurons in each Restricted Boltzmann Machine and with the optimal number of layers in the DBN during learning phase. The network structure of the learning method can be self-organized according to given input patterns of big data set. In this paper, we embed the adaptive learning method into the recurrent temporal RBM and the self-generated layer into DBN. In order to verify the effectiveness of our proposed method, the experimental results are higher classification capability than the conventional methods in this paper.

Connected through hyperedge HE-1 (induced by source paper "a video recognition method by using adaptive structural learning of long short term memory based deep belief network"):
  with members:
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (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"
    - "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:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "imaging time series to improve classification and imputation"
    - "going deeper with convolutions"

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)