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 evolution of wikipedia s norm network"
  Abstract: Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. One exception is that of the network of norms that sustain the online Wikipedia community. We study the fifteen-year evolution of this network using the interconnected set of pages that establish, describe, and interpret the community’s norms. Despite Wikipedia’s reputation for ad hoc governance, we find that its normative evolution is highly conservative. The earliest users create norms that both dominate the network and persist over time. These core norms govern both content and interpersonal interactions using abstract principles such as neutrality, verifiability, and assume good faith. As the network grows, norm neighborhoods decouple topologically from each other, while increasing in semantic coherence. Taken together, these results suggest that the evolution of Wikipedia’s norm network is akin to bureaucratic systems that predate the information age.

Connected through hyperedge HE-1 (induced by source paper "spectral based graph convolutional network for directed graphs"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "adaptive graph convolutional neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "learning deep generative models of graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"

Connected through hyperedge HE-2 (induced by source paper "designing for participation and change in digital institutions"):
  with members:
    - "laboratories of oligarchy how the iron law extends to peer production"
    - "coase s penguin or linux and the nature of the firm"

Connected through hyperedge HE-3 (induced by source paper "emergence of integrated institutions in a large population of self governing communities"):
  with members:
    - "designing for participation and change in digital institutions"
    - "laboratories of oligarchy how the iron law extends to peer production"

Connected through hyperedge HE-4 (induced by source paper "robust clustering of languages across wikipedia growth"):
  with members:
    - "understanding editing behaviors in multilingual wikipedia"
    - "trend of narratives in the age of misinformation"
    - "interactions of cultures and top people of wikipedia from ranking of 24 language editions"
    - "a practical approach to language complexity a wikipedia case study"

Connected through hyperedge HE-5 (induced by source paper "state power and elite autonomy in a networked civil society the board interlocking of chinese non profits"):
  with members:
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-6 (induced by source paper "conflict and computation on wikipedia a finite state machine analysis of editor interactions"):
  with members:
    - "analyzing organizational routines in online knowledge collaborations a case for sequence analysis in cscw"
    - "bootstrap methods for the empirical study of decision making and information flows in social systems"
    - "collective phenomena and non finite state computation in a human social system"

Connected through hyperedge HE-7 (induced by source paper "how the medium shapes the message printing and the rise of the arts and sciences"):
  with members:
    - "mapping bilateral information interests using the activity of wikipedia editors"
    - "quantifying national information interests using the activity of wikipedia editors"

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)