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: "randomized admission policy for efficient top k and frequency estimation"
  Abstract: Network management protocols often require timely and meaningful insight about per flow network traffic. This paper introduces Randomized Admission Policy (RAP) - a novel algorithm for the frequency and top-k estimation problems, which are fundamental in network monitoring. We demonstrate space reductions compared to the alternatives by a factor of up to 32 on real packet traces and up to 128 on heavy-tailed workloads. For top-k identification, RAP exhibits memory savings by a factor of between 4 and 64 depending on the skew of the workload. These empirical results are backed by formal analysis, indicating the asymptotic space improvement of our probabilistic admission approach. Additionally, we present d-Way RAP, a hardware friendly variant of RAP that empirically maintains its space and accuracy benefits.

Connected through hyperedge HE-1 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection"):
  with members:
    - "optimal elephant flow detection"
    - "heavy hitter detection entirely in the data plane"
    - "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:
    - "optimal elephant flow detection"
    - "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"
    - "lying your way to better traffic engineering"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-3 (induced by source paper "fast flow volume estimation"):
  with members:
    - "optimal elephant flow detection"
    - "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"
    - "tinylfu a highly efficient cache admission policy"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-4 (induced by source paper "constant time updates in hierarchical heavy hitters"):
  with members:
    - "optimal elephant flow detection"
    - "heavy hitter detection entirely in the data plane"
    - "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-5 (induced by source paper "give me some slack efficient network measurements"):
  with members:
    - "optimal elephant flow detection"
    - "constant time updates in hierarchical heavy hitters"
    - "efficient summing over sliding windows"
    - "sliding bloom filters"

Connected through hyperedge HE-6 (induced by source paper "pay for a sliding bloom filter and get counting distinct elements and entropy for free"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "sliding bloom filters"
    - "streaming and sublinear approximation of entropy and information distances"

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)