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 "data mining a prediction technique for the workers in the pr department of orissa block and panchayat"):
  Abstract: This paper presents the method of mining the data and which contains the information about the large information about the PR (Panchayat Raj Department) of Orissa .we have focused some of the techniques ,approaches and different methodology’s of the demand forecasting .Every organizations are operated in different places of the country. Each place of operation may generate a huge amount of data. In an organization, worker prediction is the difficult task of the manager. It is the complex process not only because its nature of feature prediction but also various approaches methodologies always makes user confused. This paper aims to deal with the problem selection process.In this paper we have used some of the approaches from literatures are been introduced and analyzed to find its suitable organization and situation, Based on this we have designed with automatic selection function to help users make a prejudgment The information about each approach will be showed to users with examples to help understanding. This system also provides calculation function to help users work out a predication result. Generally the new developed system has a more comprehensive functions compared with existing ones .It aims to improve the accuracy of demand forecasting by implementing the forecasting algorithm. While it is still a decision support system with no ability of make the final judgment . .The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas of science and engineering. To analyze, manage and make a decision of such type of huge amount of data we need techniques called the data mining which will transforming in many fields. We have implemented the algorithms in JAVA technology. This paper provides the prediction algorithm (Linear Regression, result which wil l helpful in the further research.

Includes members:
  Member "estimating continuous distributions in bayesian classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "needmining identifying micro blog data containing customer needs")
  Member "using data mining techniques for diagnosis and prognosis of cancer disease":
    also connected through other hyperedges:
      (none)
  Member "the survey of data mining applications and feature scope":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis and prediction in educational data mining a research travelogue")
  Member "soil data analysis using classification techniques and soil attribute prediction":
    also connected through other hyperedges:
      (none)

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.DB(Databases)