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: "pay for a sliding bloom filter and get counting distinct elements and entropy for free"
  Abstract: For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. Various capabilities, such as DDoS detection and load balancing, require insights about multiple metrics including Bloom filters, per-flow counting, count distinct and entropy estimation. #R##N#In this work, we present a unified construction that solves all the above problems in the sliding window model. Our single solution offers a better space to accuracy tradeoff than the state-of-the-art for each of these individual problems! We show this both analytically and by running multiple real Internet backbone and datacenter packet traces.

Connected through hyperedge HE-1 (induced by source paper "age partitioned bloom filters"):
  with members:
    - "towards intelligent compression in streams a biased reservoir sampling based bloom filter approach"

Connected through hyperedge HE-2 (induced by source paper "flattened exponential histogram for sliding window queries over data streams"):
  with members:
    - "strict very fast decision tree a memory conservative algorithm for data stream mining"
    - "efficient summing over sliding windows"

Connected through hyperedge HE-3 (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"
    - "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.DS(Data Structures and Algorithms)