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: "supporting mooc instruction with social network analysis"
  Abstract: With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. In this paper, we investigate potential reasons as to why are these digitalized learning repositories being plagued with huge attrition rates. We analyze an ongoing online course offered in Coursera using a social network perspective, with an objective to identify students who are actively participating in course discussions and those who are potentially at a risk of dropping off. We additionally perform extensive forum analysis to visualize student's posting patterns longitudinally. Our results provide insights that can assist educational designers in establishing a pedagogical basis for decision-making while designing MOOCs. We infer prominent characteristics about the participation patterns of distinct groups of students in the networked learning community, and effectively discover important discussion threads. These methods can, despite the otherwise prohibitive number of students involved, allow an instructor to leverage forum behavior to identify opportunities for support.

Connected through hyperedge HE-1 (induced by source paper "your click decides your fate inferring information processing and attrition behavior from mooc video clickstream interactions"):
  with members:
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "engaging with massive online courses"
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"
    - "together we stand together we fall together we win dynamic team formation in massive open online courses"
    - "leveraging user profile attributes for improving pedagogical accuracy of learning pathways"
    - "learning about social learning in moocs from statistical analysis to generative model"

Connected through hyperedge HE-2 (induced by source paper "who negatively influences me formalizing diffusion dynamics of negative exposure leading to student attrition in moocs"):
  with members:
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"
    - "together we stand together we fall together we win dynamic team formation in massive open online courses"
    - "the rich club phenomenon in the classroom"

Connected through hyperedge HE-3 (induced by source paper "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"):
  with members:
    - "engaging with massive online courses"
    - "learning about social learning in moocs from statistical analysis to generative model"

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.SI(Social and Information Networks)