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: "weighted random sampling over data streams"
  Abstract: In this work, we present a comprehensive treatment of weighted random sampling (WRS) over data streams. More precisely, we examine two natural interpretations of the item weights, describe an existing algorithm for each case ([2, 4]), discuss sampling with and without replacement and show adaptations of the algorithms for several WRS problems and evolving data streams.

Connected through hyperedge HE-1 (induced by source paper "communication efficient weighted reservoir sampling"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "parallel weighted random sampling"
    - "weighted sampling without replacement from data streams"
    - "fast parallel operations on search trees"
    - "practical massively parallel sorting"

Connected through hyperedge HE-2 (induced by source paper "temporally biased sampling schemes for online model management"):
  with members:
    - "temporally biased sampling for online model management"
    - "macrobase prioritizing attention in fast data"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-3 (induced by source paper "temporally biased sampling for online model management"):
  with members:
    - "macrobase prioritizing attention in fast data"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"

Connected through hyperedge HE-4 (induced by source paper "incremental skip gram model with negative sampling"):
  with members:
    - "online updating of word representations for part of speech tagging"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-5 (induced by source paper "the classical complexity of boson sampling"):
  with members:
    - "below all subsets for some permutational counting problems"

Connected through hyperedge HE-6 (induced by source paper "macrobase prioritizing attention in fast data"):
  with members:
    - "sketching linear classifiers over data streams"
    - "drop dimensionality reduction optimization for time series"
    - "asap prioritizing attention via time series smoothing"
    - "noscope optimizing neural network queries over video at scale"
    - "building efficient query engines in a high level language"
    - "incremental knowledge base construction using deepdive"
    - "graph based anomaly detection and description a survey"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-7 (induced by source paper "macrobase analytic monitoring for the internet of things"):
  with members:
    - "incremental knowledge base construction using deepdive"
    - "towards a unified architecture for in rdbms analytics"
    - "identifying the signs of fraudulent accounts using data mining techniques"

Connected through hyperedge HE-8 (induced by source paper "learning fine grained image similarity with deep ranking"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

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)