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: "convergence analysis of backpropagation algorithm for designing an intelligent system for sensing manhole gases"
  Abstract: Human fatalities are reported due to excessive proportional presence of hazardous gas components in manhole, such as hydrogen sulphide, ammonia, methane, carbon dioxide, nitrogen oxide, carbon monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN)-based intelligent sensory system was proposed for avoiding such fatalities. Backpropagation (BP) was applied for supervised training of the proposed neural network model. A gas sensor array consists of many sensor elements was employed for sensing manhole gases. Sensors in the sensor array were only responsible for sensing their target gas components. Therefore, the presence of multiple gases results in cross sensitivity that is a crucial issue to this problem. It is viewed as a pattern recognition and noise reduction problem. Various performance parameters and complexity of the problem influences NN training. In this chapter, performance of BP algorithm on such real-life application was comprehensively studied, compared, and contrasted with several hybrid intelligent approache, both in theoretical and in statistical senses.

Connected through hyperedge HE-1 (induced by source paper "identifying hazardousness of sewer pipeline gas mixture using classification methods a comparative study"):
  with members:
    - "performance analysis of neuro genetic algorithm applied on detecting proportion of components in manhole gas mixture"
    - "locally weighted naive bayes"

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)