human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "mapping the gnutella network properties of large scale peer to peer systems and implications for system design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network interdiction using adversarial traffic flows")
  Member "antnet distributed stigmergetic control for communications networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stigmergic independent reinforcement learning for multi agent collaboration")

Question: Please tell me which class the source paper 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)