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: "detecting heavy flows in the sdn match and action model"
  Abstract: Efficient algorithms and techniques to detect and identify large flows in a high throughput traffic stream in the SDN match-and-action model are presented. This is in contrast to previous work that either deviated from the match and action model by requiring additional switch level capabilities or did not exploit the SDN data plane. Our construction has two parts; (a) how to sample in an SDN match and action model, (b) how to detect large flows efficiently and in a scalable way, in the SDN model. #R##N#Our large flow detection methods provide high accuracy and present a good and practical tradeoff between switch - controller traffic, and the number of entries required in the switch flow table. Based on different parameters, we differentiate between heavy flows, elephant flows and bulky flows and present efficient algorithms to detect flows of the different types. #R##N#Additionally, as part of our heavy flow detection scheme, we present sampling methods to sample packets with arbitrary probability $p$ per packet or per byte that traverses an SDN switch. #R##N#Finally, we show how our algorithms can be adapted to a distributed monitoring SDN setting with multiple switches, and easily scale with the number of monitoring switches.

Connected through hyperedge HE-1 (induced by source paper "memento making sliding windows efficient for heavy hitters"):
  with members:
    - "optimal elephant flow detection"
    - "constant time updates in hierarchical heavy hitters"
    - "fast flow volume estimation"
    - "pay for a sliding bloom filter and get counting distinct elements and entropy for free"
    - "heavy hitter detection entirely in the data plane"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "lying your way to better traffic engineering"
    - "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)