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 "seek and push detecting large traffic aggregates in the dataplane"):
  Abstract: High level goals such as bandwidth provisioning, accounting and network anomaly detection can be easily met if high-volume traffic clusters are detected in real time. This paper presents Elastic Trie, an alternative to approaches leveraging controller-dataplane architectures. #R##N#Our solution is a novel push-based network monitoring approach that allows detection, within the dataplane, of high-volume traffic clusters. Notifications from the switch to the controller can be sent only as required, avoiding the transmission or processing of unnecessary data. Furthermore, the dataplane can iteratively refine the responsible IP prefixes allowing a controller to receive a flexible granularity information. We report and discuss an evaluation of our P4-based prototype, showing our solution to be able to detect (with 95% of precision), hierarchical heavy hitters and superspreaders using less than 8KB or 80KB of active memory respectively. Finally, Elastic Trie can identify changes in the network traffic patterns, symptomatic of Denial-of-Service attack events.

Includes members:
  Member "sonata query driven network telemetry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "measurements as first class artifacts")
  Member "fast flow volume estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memento making sliding windows efficient for heavy hitters")
  Member "heavy hitter detection entirely in the data plane":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constellation a high performance geo distributed middlebox framework")
  Member "hierarchical heavy hitters with the space saving algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memory efficient performance monitoring on programmable switches with lean algorithms")

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.NI(Networking and Internet Architecture)