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: "mining educational data to analyze students performance"
  Abstract: The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.

Connected through hyperedge HE-1 (induced by source paper "collaboration analysis using deep learning"):
  with members:
    - "mask r cnn"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a multi hidden recurrent neural network with a modified grey wolf optimizer"):
  with members:
    - "lecturer performance system using neural network with particle swarm optimization"

Connected through hyperedge HE-3 (induced by source paper "the role of artificial intelligence ai in adaptive elearning system aes content formation risks and opportunities involved"):
  with members:
    - "modelling the effectiveness of curriculum in educational systems using bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "educational data mining and learning analytics educational assistance for teaching and learning"):
  with members:
    - "a study on feature selection techniques in educational data mining"

Connected through hyperedge HE-5 (induced by source paper "student dropout risk assessment in undergraduate course at residential university"):
  with members:
    - "data mining applications a comparative study for predicting student s 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-6 (induced by source paper "building a classification model for enrollment in higher educational courses using data mining techniques"):
  with members:
    - "data mining applications a comparative study for predicting student s performance"
    - "a data mining view on class room teaching language"
    - "data mining a prediction of performer or underperformer using classification"

Connected through hyperedge HE-7 (induced by source paper "learning analytics a survey"):
  with members:
    - "predicting students performance using id3 and c4 5 classification algorithms"

Connected through hyperedge HE-8 (induced by source paper "mining education data to predict student s retention a comparative study"):
  with members:
    - "data mining applications a comparative study for predicting student s 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"

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)