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: "robustness of social networks comparative results based on distance distributions"
  Abstract: Given a social network, which of its nodes have a stronger impact in determining its structure? More formally: which node-removal order has the greatest impact on the network structure? We approach this well-known problem for the first time in a setting that combines both web graphs and social networks, using datasets that are orders of magnitude larger than those appearing in the previous literature, thanks to some recently developed algorithms and software tools that make it possible to approximate accurately the number of reachable pairs and the distribution of distances in a graph. Our experiments highlight deep differences in the structure of social networks and web graphs, show significant limitations of previous experimental results, and at the same time reveal clustering by label propagation as a new and very effective way of locating nodes that are important from a structural viewpoint.

Connected through hyperedge HE-1 (induced by source paper "computing classic closeness centrality at scale"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "axioms for centrality"
    - "in core computation of geometric centralities with hyperball a hundred billion nodes and beyond"
    - "defining and evaluating network communities based on ground truth"
    - "four degrees of separation"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-2 (induced by source paper "all distances sketches revisited hip estimators for massive graphs analysis"):
  with members:
    - "timed influence computation and maximization"
    - "computing classic closeness centrality at scale"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "axioms for centrality"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "four degrees of separation"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "four degrees of separation really"):
  with members:
    - "four degrees of separation"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"

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)