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: "cr precis a deterministic summary structure for update data streams"
  Abstract: We present the \crprecis structure, that is a general-purpose, deterministic and sub-linear data structure for summarizing \emph{update} data streams. The \crprecis structure yields the \emph{first deterministic sub-linear space/time algorithms for update streams} for answering a variety of fundamental stream queries, such as, (a) point queries, (b) range queries, (c) finding approximate frequent items, (d) finding approximate quantiles, (e) finding approximate hierarchical heavy hitters, (f) estimating inner-products, (g) near-optimal $B$-bucket histograms, etc..

Connected through hyperedge HE-1 (induced by source paper "on low risk heavy hitters and sparse recovery schemes"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "clustering high dimensional dynamic data streams"
    - "heavy hitters via cluster preserving clustering"
    - "restricted isometry property for general p norms"
    - "for all sparse recovery in near optimal time"
    - "compressive sensing with local geometric features"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "on the power of adaptivity in sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "variability in data streams"):
  with members:
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for distributed functional monitoring"
    - "optimal tracking of distributed heavy hitters and quantiles"

Connected through hyperedge HE-3 (induced by source paper "time lower bounds for nonadaptive turnstile streaming algorithms"):
  with members:
    - "cell probe bounds for online edit distance and other pattern matching problems"
    - "parameterized streaming algorithms for vertex cover"
    - "tight cell probe bounds for online hamming distance computation"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "fast moment estimation in data streams in optimal space"
    - "lower bounds on near neighbor search via metric expansion"
    - "sketching and streaming entropy via approximation theory"
    - "logarithmic lower bounds in the cell probe model"

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)