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: "constant time updates in hierarchical heavy hitters"
  Abstract: Monitoring tasks, such as anomaly and DDoS detection, require identifying frequent flow aggregates based on common IP prefixes. These are known as hierarchical heavy hitters (HHH), where the hierarchy is determined based on the type of prefixes of interest in a given application. The per packet complexity of existing HHH algorithms is proportional to the size of the hierarchy, imposing significant overheads.   In this paper, we propose a randomized constant time algorithm for HHH. We prove probabilistic precision bounds backed by an empirical evaluation. Using four real Internet packet traces, we demonstrate that our algorithm indeed obtains comparable accuracy and recall as previous works, while running up to 62 times faster. Finally, we extended Open vSwitch (OVS) with our algorithm and showed it is able to handle 13.8 million packets per second. In contrast, incorporating previous works in OVS only obtained 2.5 times lower throughput.

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"
    - "heavy hitter detection entirely in the data plane"
    - "dapper data plane performance diagnosis of tcp"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-2 (induced by source paper "locality sensitive sketching for resilient network flow monitoring"):
  with members:
    - "heavy hitter detection entirely in the data plane"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "elastic_hh tailored elastic for finding heavy hitters"):
  with members:
    - "heavy hitter detection entirely in the data plane"

Connected through hyperedge HE-4 (induced by source paper "memento making sliding windows efficient for heavy hitters"):
  with members:
    - "optimal elephant flow detection"
    - "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-5 (induced by source paper "heavy hitters over interval queries"):
  with members:
    - "a high performance algorithm for identifying frequent items in data streams"
    - "hierarchical heavy hitters with the space saving algorithm"
    - "applications of data mining to electronic commerce"

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"
    - "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-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"
    - "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-8 (induced by source paper "give me some slack efficient network measurements"):
  with members:
    - "optimal elephant flow detection"
    - "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)