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: "optimal elephant flow detection"
  Abstract: Monitoring the traffic volumes of elephant flows, including the total byte count per flow, is a fundamental capability for online network measurements. We present an asymptotically optimal algorithm for solving this problem in terms of both space and time complexity. This improves on previous approaches, which can only count the number of packets in constant time. We evaluate our work on real packet traces, demonstrating an up to X2.5 speedup compared to the best alternative.

Connected through hyperedge HE-1 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection"):
  with members:
    - "heavy hitter detection entirely in the data plane"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "amon an open source architecture for online monitoring statistical analysis and forensics of multi gigabit streams"
    - "invertible bloom lookup tables"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-2 (induced by source paper "memento making sliding windows efficient for heavy hitters"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "detecting heavy flows in the sdn match and action model"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a high performance algorithm for identifying frequent items in data streams"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "smoking out the heavy hitter flows with hashpipe"
    - "beating countsketch for heavy hitters in insertion streams"
    - "simple and deterministic matrix sketching"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-4 (induced by source paper "fast flow volume estimation"):
  with members:
    - "a high performance algorithm for identifying frequent items in data streams"
    - "constant time updates in hierarchical heavy hitters"
    - "heavy hitter detection entirely in the data plane"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "tinylfu a highly efficient cache admission policy"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-5 (induced by source paper "constant time updates in hierarchical heavy hitters"):
  with members:
    - "heavy hitter detection entirely in the data plane"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "moongen a scriptable high speed packet generator"
    - "tinylfu a highly efficient cache admission policy"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-6 (induced by source paper "give me some slack efficient network measurements"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "efficient summing over sliding windows"
    - "sliding bloom filters"

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.DS(Data Structures and Algorithms)