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: "sonata query driven network telemetry"
  Abstract: Operating networks depends on collecting and analyzing measurement data. Current technologies do not make it easy to do so, typically because they separate data collection (e.g., packet capture or flow monitoring) from analysis, producing either too much data to answer a general question or too little data to answer a detailed question. In this paper, we present Sonata, a network telemetry system that uses a uniform query interface to drive the joint collection and analysis of network traffic. Sonata takes the advantage of two emerging technologies---streaming analytics platforms and programmable network devices---to facilitate joint collection and analysis. Sonata allows operators to more directly express network traffic analysis tasks in terms of a high-level language. The underlying runtime partitions each query into a portion that runs on the switch and another that runs on the streaming analytics platform iteratively refines the query to efficiently capture only the traffic that pertains to the operator's query, and exploits sketches to reduce state in switches in exchange for more approximate results. Through an evaluation of a prototype implementation, we demonstrate that Sonata can support a wide range of network telemetry tasks with less state in the network, and lower data rates to streaming analytics systems, than current approaches can achieve.

Connected through hyperedge HE-1 (induced by source paper "measurements as first class artifacts"):
  with members:
    - "the case for data plane timestamping in sdn"
    - "principles for measurability in protocol design"
    - "dapper data plane performance diagnosis of tcp"

Connected through hyperedge HE-2 (induced by source paper "seek and push detecting large traffic aggregates in the dataplane"):
  with members:
    - "fast flow volume estimation"
    - "heavy hitter detection entirely in the data plane"
    - "hierarchical heavy hitters with the space saving algorithm"

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)