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: "deep diving into bittorrent locality"
  Abstract: A substantial amount of work has recently gone into localizing BitTorrent traffic within an ISP in order to avoid excessive and often times unnecessary transit costs. Several architectures and systems have been proposed and the initial results from specific ISPs and a few torrents have been encouraging. In this work we attempt to deepen and scale our understanding of locality and its potential. Looking at specific ISPs, we consider tens of thousands of concurrent torrents, and thus capture ISP-wide implications that cannot be appreciated by looking at only a handful of torrents. Secondly, we go beyond individual case studies and present results for the top 100 ISPs in terms of number of users represented in our dataset of up to 40K torrents involving more than 3.9M concurrent peers and more than 20M in the course of a day spread in 11K ASes. We develop scalable methodologies that permit us to process this huge dataset and answer questions such as: "\emph{what is the minimum and the maximum transit traffic reduction across hundreds of ISPs?}", "\emph{what are the win-win boundaries for ISPs and their users?}", "\emph{what is the maximum amount of transit traffic that can be localized without requiring fine-grained control of inter-AS overlay connections?}", "\emph{what is the impact to transit traffic from upgrades of residential broadband speeds?}".

Connected through hyperedge HE-1 (induced by source paper "tardis stably shifting traffic in space and time extended version"):
  with members:
    - "content aware traffic engineering"

Connected through hyperedge HE-2 (induced by source paper "privacy in content oriented networking threats and countermeasures"):
  with members:
    - "dos and ddos in named data networking"
    - "securing instrumented environments over content centric networking the case of lighting control"
    - "one bad apple spoils the bunch exploiting p2p applications to trace and profile tor users"
    - "andana anonymous named data networking application"
    - "user relative names for globally connected personal devices"

Connected through hyperedge HE-3 (induced by source paper "pushing bittorrent locality to the limit"):
  with members:
    - "bittorrent experiments on testbeds a study of the impact of network latencies"
    - "rarest first and choke algorithms are enough"
    - "clustering and sharing incentives in bittorrent systems"

Connected through hyperedge HE-4 (induced by source paper "fakedetector a measurement based tool to get rid out of fake content in your bittorrent downloads"):
  with members:
    - "reinforcement learning in bittorrent systems"
    - "is content publishing in bittorrent altruistic or profit driven"

Connected through hyperedge HE-5 (induced by source paper "is content publishing in bittorrent altruistic or profit driven"):
  with members:
    - "unraveling bittorrent s file unavailability measurements analysis and solution exploration"
    - "clustering and sharing incentives in bittorrent systems"

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)