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: "stream sampling for frequency cap statistics"
  Abstract: Unaggregated data, in streamed or distributed form, is prevalent and come from diverse application domains which include interactions of users with web services and IP traffic. Data elements have {\em keys} (cookies, users, queries) and elements with different keys interleave. Analytics on such data typically utilizes statistics stated in terms of the frequencies of keys. The two most common statistics are {\em distinct}, which is the number of active keys in a specified segment, and {\em sum}, which is the sum of the frequencies of keys in the segment. Both are special cases of {\em cap} statistics, defined as the sum of frequencies {\em capped} by a parameter $T$, which are popular in online advertising platforms. Aggregation by key, however, is costly, requiring state proportional to the number of distinct keys, and therefore we are interested in estimating these statistics or more generally, sampling the data, without aggregation. We present a sampling framework for unaggregated data that uses a single pass (for streams) or two passes (for distributed data) and state proportional to the desired sample size. Our design provides the first effective solution for general frequency cap statistics. Our $\ell$-capped samples provide estimates with tight statistical guarantees for cap statistics with $T=\Theta(\ell)$ and nonnegative unbiased estimates of {\em any} monotone non-decreasing frequency statistics. An added benefit of our unified design is facilitating {\em multi-objective samples}, which provide estimates with statistical guarantees for a specified set of different statistics, using a single, smaller sample.

Connected through hyperedge HE-1 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "maximum matching in turnstile streams"
    - "structure aware sampling flexible and accurate summarization"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "perfect l_p sampling in a data stream"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "heavy hitters via cluster preserving clustering"
    - "pseudorandomness via the discrete fourier transform"
    - "beating countsketch for heavy hitters in insertion streams"
    - "improved concentration bounds for count sketch"
    - "structure aware sampling flexible and accurate summarization"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-3 (induced by source paper "composite hashing for data stream sketches"):
  with members:
    - "on sampling from massive graph streams"
    - "learning to hash for indexing big data a survey"
    - "gsketch on query estimation in graph streams"

Connected through hyperedge HE-4 (induced by source paper "stream aggregation through order sampling"):
  with members:
    - "on sampling from massive graph streams"
    - "efficient sampling for better osn data provisioning"
    - "graph sample and hold a framework for big graph analytics"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-5 (induced by source paper "estimating frequency statistics through distinct count measurements"):
  with members:
    - "multi objective weighted sampling"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "structure aware sampling flexible and accurate summarization"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-6 (induced by source paper "multi objective weighted sampling"):
  with members:
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "stream sampling for variance optimal estimation of subset sums"

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.IR(Information Retrieval)