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: "sliding bloom filters"
  Abstract: A Bloom filter is a method for reducing the space (memory) required for representing a set by allowing a small error probability. In this paper we consider a \emph{Sliding Bloom Filter}: a data structure that, given a stream of elements, supports membership queries of the set of the last $n$ elements (a sliding window), while allowing a small error probability. We formally define the data structure and its relevant parameters and analyze the time and memory requirements needed to achieve them. We give a low space construction that runs in O(1) time per update with high probability (that is, for all sequences with high probability all operations take constant time) and provide an almost matching lower bound on the space that shows that our construction has the best possible space consumption up to an additive lower order term.

Connected through hyperedge HE-1 (induced by source paper "give me some slack efficient network measurements"):
  with members:
    - "optimal elephant flow detection"
    - "constant time updates in hierarchical heavy hitters"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "efficient summing over sliding windows"

Connected through hyperedge HE-2 (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"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "streaming and sublinear approximation of entropy and information distances"

Connected through hyperedge HE-3 (induced by source paper "bloom filters in adversarial environments"):
  with members:
    - "how to approximate a set without knowing its size in advance"
    - "how robust are linear sketches to adaptive inputs"
    - "explicit and efficient hash families suffice for cuckoo hashing with a stash"
    - "don t thrash how to cache your hash on flash"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-4 (induced by source paper "how to approximate a set without knowing its size in advance"):
  with members:
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "succinct data structures for retrieval and approximate membership"
    - "an optimal bloom filter replacement based on matrix solving"

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)