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: "hierarchical heavy hitters with the space saving algorithm"
  Abstract: The Hierarchical Heavy Hitters problem extends the notion of frequent items to data arranged in a hierarchy. This problem has applications to network traffic monitoring, anomaly detection, and DDoS detection. We present a new streaming approximation algorithm for computing Hierarchical Heavy Hitters that has several advantages over previous algorithms. It improves on the worst-case time and space bounds of earlier algorithms, is conceptually simple and substantially easier to implement, offers improved accuracy guarantees, is easily adopted to a distributed or parallel setting, and can be efficiently implemented in commodity hardware such as ternary content addressable memory (TCAMs). We present experimental results showing that for parameters of primary practical interest, our two-dimensional algorithm is superior to existing algorithms in terms of speed and accuracy, and competitive in terms of space, while our one-dimensional algorithm is also superior in terms of speed and accuracy for a more limited range of parameters.

Connected through hyperedge HE-1 (induced by source paper "memory efficient performance monitoring on programmable switches with lean algorithms"):
  with members:
    - "optimizing bloom filter challenges solutions and comparisons"
    - "constant time updates in hierarchical heavy hitters"
    - "heavy hitter detection entirely in the data plane"
    - "dapper data plane performance diagnosis of tcp"

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"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "lying your way to better traffic engineering"

Connected through hyperedge HE-3 (induced by source paper "heavy hitters over interval queries"):
  with members:
    - "a high performance algorithm for identifying frequent items in data streams"
    - "constant time updates in hierarchical heavy hitters"
    - "applications of data mining to electronic commerce"

Connected through hyperedge HE-4 (induced by source paper "seek and push detecting large traffic aggregates in the dataplane"):
  with members:
    - "sonata query driven network telemetry"
    - "fast flow volume estimation"
    - "heavy hitter detection entirely in the data plane"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "graph constrained group testing"

Connected through hyperedge HE-6 (induced by source paper "a high performance algorithm for identifying frequent items in data streams"):
  with members:
    - "optimal elephant flow detection"
    - "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"

Connected through hyperedge HE-7 (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"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "tinylfu a highly efficient cache admission policy"

Connected through hyperedge HE-8 (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"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "moongen a scriptable high speed packet generator"
    - "tinylfu a highly efficient cache admission policy"

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)