human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "knowledge extracted from recurrent deep belief network for real time deterministic control"):
  Abstract: Recently, the market on deep learning including not only software but also hardware is developing rapidly. Big data is collected through IoT devices and the industry world will analyze them to improve their manufacturing process. Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Although deep learning can show the high capability of classification, prediction, and so on, the implementation on GPU devices are required. We may meet the trade-off between the higher precision by deep learning and the higher cost with GPU devices. We can success the knowledge extraction from the trained deep learning with high classification capability. The knowledge that can realize faster inference of pre-trained deep network is extracted as IF-THEN rules from the network signal flow given input data. Some experiment results with benchmark tests for time series data sets showed the effectiveness of our proposed method related to the computational speed.

Includes members:
  Member "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm":
    also connected through other hyperedges:
      - 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")
  Member "adaptive learning method of recurrent temporal deep belief network to analyze time series data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a video recognition method by using adaptive structural learning of long short term memory based deep belief network")
  Member "an adaptive learning method of deep belief network by layer generation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shortening time required for adaptive structural learning method of deep belief network with multi modal data arrangement")
  Member "fine tuning method by using knowledge acquisition from deep belief network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive structural learning of deep belief network for medical examination data and its knowledge extraction by using c4 5")
  Member "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")
  Member "improved use of continuous attributes in c4 5":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process")

Question: Please tell me which class the source paper 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)