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: "understanding the group dynamics and success of teams"
  Abstract: Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data. We analyse activity traces and success levels for approximately 150 000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or ‘leads’ of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavours.

Connected through hyperedge HE-1 (induced by source paper "china may need to support more small teams in scientific research"):
  with members:
    - "practical method to reclassify web of science articles into unique subject categories and broad disciplines"
    - "the preeminence of ethnic diversity in scientific collaboration"
    - "collaboration diversity and scientific impact"
    - "funding acknowledgment analysis queries and caveats"
    - "the relationship among research productivity research collaboration and their determinants"
    - "characterization description and considerations for the use of funding acknowledgement data in web of science"
    - "field normalized citation impact indicators and the choice of an appropriate counting method"

Connected through hyperedge HE-2 (induced by source paper "massive multi agent data driven simulations of the github ecosystem"):
  with members:
    - "predicting and explaining behavioral data with structured feature space decomposition"
    - "predicting the popularity of github repositories"
    - "coding together at scale github as a collaborative social network"

Connected through hyperedge HE-3 (induced by source paper "collaboration drives individual productivity"):
  with members:
    - "predicting and explaining behavioral data with structured feature space decomposition"
    - "information overload in group communication from conversation to cacophony in the twitch chat"
    - "aristotle vs ringelmann a response to scholtes et al on superlinear production in open source software"

Connected through hyperedge HE-4 (induced by source paper "an information theoretic all scales approach to comparing networks"):
  with members:
    - "characterizing the structural diversity of complex networks across domains"
    - "learning convolutional neural networks for graphs"

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)