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: "multiple random walks to uncover short paths in power law networks"
  Abstract: Developing simple distributed algorithms to allow nodes to perform topology discovery and message routing using incomplete topological information is a problem of great interest in network science. Consider the following routing problem in the context of a large power law network G. A small number of nodes want to exchange messages over short paths on G. These nodes do not have access to the topology of G but are allowed to crawl the network subject to a budget constraint. Only crawlers whose paths cross are allowed to exchange topological information. In this work we study the use of random walks (RWs) to crawl G. We show that RWs have the ability to find short paths and that this bears no relation to the paths that they take. Instead, it relies on two properties of RWs on power law networks: • The RW ability to observe a sizable fraction of the network edges; • The near certainty that two distinct RW sample paths cross after a small percentage of the nodes have been visited. We show promising simulation results on several real world networks.

Connected through hyperedge HE-1 (induced by source paper "practical characterization of large networks using neighborhood information"):
  with members:
    - "network sampling from static to streaming graphs"
    - "towards unbiased bfs sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)