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: "a hybrid sampling scheme for triangle counting"
  Abstract: We study the problem of estimating the number of triangles in a graph stream. No streaming algorithm can get sublinear space on all graphs, so methods in this area bound the space in terms of parameters of the input graph such as the maximum number of triangles sharing a single edge. We give a sampling algorithm that is additionally parameterized by the maximum number of triangles sharing a single vertex. Our bound matches the best known turnstile results in all graphs, and gets better performance on simple graphs like $G(n, p)$ or a set of independent triangles.  We complement the upper bound with a lower bound showing that no sampling algorithm can do better on those graphs by more than a log factor. In particular, any insertion stream algorithm must use $\sqrt{T}$ space when all the triangles share a common vertex, and any sampling algorithm must take $T^\frac{1}{3}$ samples when all the triangles are independent. We add another lower bound, also matching our algorithm's performance, which applies to all graph classes. This lower bound covers "triangle-dependent" sampling algorithms, a subclass that includes our algorithm and all previous sampling algorithms for the problem.  Finally, we show how to generalize our algorithm to count arbitrary subgraphs of constant size.

Connected through hyperedge HE-1 (induced by source paper "exponential separations between turnstile streaming and linear sketching"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "data streams with bounded deletions"
    - "optimality of linear sketching under modular updates"
    - "on estimating maximum matching size in graph streams"
    - "linear sketching over mathbb f_2"
    - "maximum matching in turnstile streams"

Connected through hyperedge HE-2 (induced by source paper "cocos fast and accurate distributed triangle counting in graph streams"):
  with members:
    - "on sampling from massive graph streams"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "graph sample and hold a framework for big graph analytics"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "defining and evaluating network communities based on ground truth"
    - "colorful triangle counting and a mapreduce implementation"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "on the multiparty communication complexity of testing triangle freeness"):
  with members:
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "distributed testing of excluded subgraphs"
    - "fast distributed algorithms for testing graph properties"
    - "dynamic graph stream algorithms in o n space"
    - "approximately counting triangles in sublinear time"
    - "algebraic methods in the congested clique"
    - "optimal principal component analysis in distributed and streaming models"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

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)