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: "fine tuning method by using knowledge acquisition from deep belief network"
  Abstract: We developed an adaptive structure learning method of Restricted Boltzmann Machine (RBM) which can generate/annihilate neurons by self-organizing learning method according to input patterns. Moreover, the adaptive Deep Belief Network (DBN) in the assemble process of pre-trained RBM layer was developed. The proposed method presents to score a great success to the training data set for big data benchmark test such as CIFAR-10. However, the classification capability of the test data set, which are included unknown patterns, is high, but does not lead perfect correct solution. We investigated the wrong specified data and then some characteristic patterns were found. In this paper, the knowledge related to the patterns is embedded into the classification algorithm of trained DBN. As a result, the classification capability can achieve a great success (97.1% to unknown data set).

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:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
    - "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:
    - "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"
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "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 "an adaptive learning method of deep belief network by layer generation algorithm"):
  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)