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: "data mining applications a comparative study for predicting student s performance"
  Abstract: Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

Connected through hyperedge HE-1 (induced by source paper "student dropout risk assessment in undergraduate course at residential university"):
  with members:
    - "mining educational data to analyze students performance"
    - "data mining a prediction for performance improvement using classification"
    - "a data mining view on class room teaching language"
    - "a study on feature selection techniques in educational data mining"

Connected through hyperedge HE-2 (induced by source paper "building a classification model for enrollment in higher educational courses using data mining techniques"):
  with members:
    - "mining educational data to analyze students performance"
    - "a data mining view on class room teaching language"
    - "data mining a prediction of performer or underperformer using classification"

Connected through hyperedge HE-3 (induced by source paper "the application of data mining to build classification model for predicting graduate employment"):
  with members:
    - "data mining a prediction for performance improvement using classification"

Connected through hyperedge HE-4 (induced by source paper "mining education data to predict student s retention a comparative study"):
  with members:
    - "mining educational data to analyze students performance"
    - "data mining a prediction for performance improvement using classification"
    - "a data mining view on class room teaching language"
    - "data mining a prediction of performer or underperformer using classification"

Connected through hyperedge HE-5 (induced by source paper "data mining application to attract students in hei"):
  with members:
    - "data mining as a torch bearer in education sector"
    - "mining educational data to analyze students performance"
    - "a data mining view on class room teaching language"

Connected through hyperedge HE-6 (induced by source paper "mining educational data using classification to decrease dropout rate of students"):
  with members:
    - "mining educational data to analyze students performance"
    - "a data mining view on class room teaching language"

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.IR(Information Retrieval)