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: "the rich club phenomenon in the classroom"
  Abstract: More than 1.2 million students drop out of school every year in the U.S., one every 26 seconds1. Year 2007 dropouts will cost more than $300 billion in lost wages, taxes and productivity to the U.S. Dropouts contribute about $60,000 less in federal and state income taxes. Each cohort of dropouts costs the U.S. $192 billion in lost income and taxes2. A dropout student is more than 8 times as likely to be in jail or prison as a high school graduate and nearly 20 times as likely as a college graduate3.#R##N##R##N#Early detection of poor performance will allow more time to take corrective actions and will likely help to reduce the number of dropouts. Therefore, it is of the utmost importance to be able to assess the performance of students in a continuous manner.#R##N##R##N#Computer science is not unaware of this need for close follow up of students. Computer Supported Collaborative Learning (CSCL) is a branch of computer science that intersects with pedagogy and social sciences. Indeed, one of the goals of CSCL is to explore appropriate methods/tools for evaluating collaboration so that more insight can be gained into the results of lecturing/teaching procedures4.#R##N##R##N#However, systematic gathering and analysis of educational data in-natura has only recently started. So far this analysis has mainly tried to determine static structural features of the social learning network formed by the students. For instance, Nurmela et al. looked at the structure of the interactions trying to determine the central actors in a CSCL environment5. In this social structure, “key communicators” were assumed to be the most connected individuals in time-aggregated networks6. Similar analyses were carried out by Martinez et al.7 and Chen and Watanabe, who focused on other structural parameters that are important for the final score: group structure, member's physical location distribution, and member's social position8.#R##N##R##N#Beyond this merely static structural analysis, the literature also highlights the key role of student interaction for effective learning. At a societal scale, Granovetter's pioneering work9 recognised the importance of interaction patterns and proposed his well-known “strength of weak ties” phenomenon, where he hypothesised that isolated social ties offer limited access to external prospects, while heterogeneous social ties diversify one's opportunities.#R##N##R##N#While the relevance of the social network structure and interactions has been widely recognised in the educational context10, some other factors have recently been under the spotlight, e.g. social acceptance or willingness to communicate11. In general, it is not just about knowing “who” the students interact with, but “how” and “when” they do it and, importantly, what is the result of these interactions with regards to the educational outcome12.#R##N##R##N#Preliminary answers to the “how” question come from different works. The effects of analysing the relationships between web forum users on the structure of the network (reconstructed from the messages sent) were studied in13,14. Also, the type of interaction or content being exchanged have been considered6,16. However, these previous analyses were based on a static snapshot of the structure and interactions of the network at some point in time or included a reduced number of samples. For instance7, analysed these macroscopic metrics in the four different assignments the course was structured in ( once a month).#R##N##R##N#Acquiring full knowledge on “how” students interact would be facilitated by having access to dynamic interactions and their changes with time. Timing is a determinant element to understand the correspondence between student behaviour and performance. Therefore, this paper tries to determine the individual and group-level behavioural patterns that lead to low scoring and possible dropout. Gaining insight into these data could help in identifying “groups at risk”, enabling educators to act sooner and hopefully reduce dropout rates.#R##N##R##N#The rest of this paper is organised as follows. Next section presents the main results obtained from our analysis. This is followed by a broader discussion.

Connected through hyperedge HE-1 (induced by source paper "a survey on studying the social networks of students"):
  with members:
    - "global rank estimation"
    - "social network analysis of the caste based reservation system in india"
    - "together we stand together we fall together we win dynamic team formation in massive open online courses"

Connected through hyperedge HE-2 (induced by source paper "towards value sensitive learning analytics design"):
  with members:
    - "group communication analysis a computational linguistics approach for detecting sociocognitive roles in multiparty interactions"
    - "beyond opening up the black box investigating the role of algorithmic systems in wikipedian organizational culture"

Connected through hyperedge HE-3 (induced by source paper "understanding communication patterns in moocs combining data mining and qualitative methods"):
  with members:
    - "communication communities in moocs"

Connected through hyperedge HE-4 (induced by source paper "who negatively influences me formalizing diffusion dynamics of negative exposure leading to student attrition in moocs"):
  with members:
    - "supporting mooc instruction with social network analysis"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "communication communities in moocs"):
  with members:
    - "tuned models of peer assessment in moocs"
    - "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)