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: "assembling thefacebook using heterogeneity to understand online social network assembly"
  Abstract: Online social networks represent a popular and diverse class of social media systems. Despite this variety, each of these systems undergoes a general process of online social network assembly, which represents the complicated and heterogeneous changes that transform newly born systems into mature platforms. However, little is known about this process. For example, how much of a network's assembly is driven by simple growth? How does a network's structure change as it matures? How does network structure vary with adoption rates and user heterogeneity, and do these properties play different roles at different points in the assembly? We investigate these and other questions using a unique dataset of online connections among the roughly one million users at the first 100 colleges admitted to Facebook, captured just 20 months after its launch. We first show that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth. We then exploit natural experiments embedded in this dataset and complementary data obtained via Internet archaeology to show that different subnetworks matured at different rates toward similar end states. These results shed light on the processes and patterns of online social network assembly, and may facilitate more effective design for online social systems.

Connected through hyperedge HE-1 (induced by source paper "learning resolution parameters for graph clustering"):
  with members:
    - "a parallel projection method for metric constrained optimization"
    - "correlation clustering generalized"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"
    - "an optimization approach to locally biased graph algorithms"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "network constraints on the mixing patterns of binary node metadata"):
  with members:
    - "multiscale mixing patterns in networks"
    - "the ground truth about metadata and community detection in networks"
    - "the majority illusion in social networks"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-3 (induced by source paper "to stay or to leave churn prediction for urban migrants in the initial period"):
  with members:
    - "urban dreams of migrants a case study of migrant integration in shanghai"
    - "motifs in temporal networks"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "twitter as a source of global mobility patterns for social good"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "the digital life of walkable streets"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-4 (induced by source paper "assembly in populations of social networks"):
  with members:
    - "emergence of complex institutions in a large population of self governing communities"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "laboratories of oligarchy how the iron law extends to peer production"
    - "friendship networks and social status"

Connected through hyperedge HE-5 (induced by source paper "motifs in temporal networks"):
  with members:
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

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)