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: "pushing bittorrent locality to the limit"
  Abstract: Peer-to-peer (P2P) locality has recently raised a lot of interest in the community. Indeed, whereas P2P content distribution enables financial savings for the content providers, it dramatically increases the traffic on inter-ISP links.To solve this issue, the idea to keep a fraction of the P2P traffic local to each ISP was introduced a few years ago. Since then, P2P solutions exploiting locality have been introduced. However, several fundamental issues on locality still need to be explored. In particular, how far can we push locality, and what is, at the scale of the Internet, the reduction of traffic that can be achieved with locality?In this paper, we perform extensive experiments on a controlled environment with up to 10,000 BitTorrent clients to evaluate the impact of high locality on inter-ISP links traffic and peers download completion time.We introduce two simple mechanisms that make high locality possible in challenging scenarios and we show that we save up to several orders of magnitude inter-ISP traffic compared to traditional locality without adversely impacting peers download completion time. In addition, we crawled 214,443 torrents representing 6,113,224 unique peers spread among 9605ASes. We show that whereas the torrents we crawled generated 11.6 petabytes of inter-ISP traffic, our locality policy implemented for all torrents could have reduced the global inter-ISP traffic by up to 40%.

Connected through hyperedge HE-1 (induced by source paper "isp friendly peer assisted on demand streaming of long duration content in bbc iplayer"):
  with members:
    - "clustering and sharing incentives in bittorrent systems"

Connected through hyperedge HE-2 (induced by source paper "analyzing peer selection policies for bittorrent multimedia on demand streaming systems in internet"):
  with members:
    - "on client interactive behaviour to design peer selection policies for bittorrent like protocols"
    - "heterogeneous download times in a homogeneous bittorrent swarm"
    - "rarest first and choke algorithms are enough"

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.NI(Networking and Internet Architecture)