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 "memento making sliding windows efficient for heavy hitters"):
  Abstract: Cloud operators require real-time identification of Heavy Hitters (HH) and Hierarchical Heavy Hitters (HHH) for applications such as load balancing, traffic engineering, and attack mitigation. However, existing techniques are slow in detecting new heavy hitters. #R##N#In this paper, we make the case for identifying heavy hitters through \textit{sliding windows}. Sliding windows detect heavy hitters quicker and more accurately than current methods, but to date had no practical algorithms. Accordingly, we introduce, design and analyze the \textit{Memento} family of sliding window algorithms for the HH and HHH problems in the single-device and network-wide settings. Using extensive evaluations, we show that our single-device solutions attain similar accuracy and are by up to $273\times$ faster than existing window-based techniques. Furthermore, we exemplify our network-wide HHH detection capabilities on a realistic testbed. To that end, we implemented Memento as an open-source extension to the popular HAProxy cloud load-balancer. In our evaluations, using an HTTP flood by 50 subnets, our network-wide approach detected the new subnets faster, and reduced the number of undetected flood requests by up to $37\times$ compared to the alternatives.

Includes members:
  Member "optimal elephant flow detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection")
  Member "constant time updates in hierarchical heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory efficient performance monitoring on programmable switches with lean algorithms")
  Member "detecting heavy flows in the sdn match and action model":
    also connected through other hyperedges:
      (none)
  Member "fast flow volume estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seek and push detecting large traffic aggregates in the dataplane")
  Member "pay for a sliding bloom filter and get counting distinct elements and entropy for free":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "age partitioned bloom filters")
  Member "heavy hitter detection entirely in the data plane":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constellation a high performance geo distributed middlebox framework")
  Member "randomized admission policy for efficient top k and frequency estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection")
  Member "lying your way to better traffic engineering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deltapath dataflow based high performance incremental routing")

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)