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: "a linear streaming algorithm for community detection in very large networks"
  Abstract: In this paper, we introduce a novel community detection algorithm in graphs, called SCoDA (Streaming Community Detection Algorithm), based on an edge streaming setting. This algorithm has an extremely low memory footprint and a lightning-fast execution time as it only stores two integers per node and processes each edge strictly once. The approach is based on the following simple observation: if we pick an edge uniformly at random in the network, this edge is more likely to connect two nodes of the same community than two nodes of distinct communities. We exploit this idea to build communities by local changes at each edge arrival. Using theoretical arguments, we relate the ability of SCoDA to detect communities to usual quality metrics of these communities like the conductance. Experimental results performed on massive real-life networks ranging from one million to more than one billion edges shows that SCoDA runs more than ten times faster than existing algorithms and leads to similar or better detection scores on the largest graphs.

Connected through hyperedge HE-1 (induced by source paper "dyncomm r package dynamic community detection for evolving networks"):
  with members:
    - "density based community detection optimization"
    - "community discovery in dynamic networks a survey"
    - "streaming memory limited algorithms for community detection"
    - "community detection via random and adaptive sampling"

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)