human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  Abstract: We study sublinear algorithms for two fundamental graph problems, MAXCUT and correlation clustering. Our focus is on constructing core-sets as well as developing streaming algorithms for these problems. Constant space algorithms are known for dense graphs for these problems, while $\Omega(n)$ lower bounds exist (in the streaming setting) for sparse graphs. #R##N#Our goal in this paper is to bridge the gap between these extremes. Our first result is to construct core-sets of size $\tilde{O}(n^{1-\delta})$ for both the problems, on graphs with average degree $n^{\delta}$ (for any $\delta >0$). This turns out to be optimal, under the exponential time hypothesis (ETH). Our core-set analysis is based on studying random-induced sub-problems of optimization problems. To the best of our knowledge, all the known results in our parameter range rely crucially on near-regularity assumptions. We avoid these by using a biased sampling approach, which we analyze using recent results on concentration of quadratic functions. We then show that our construction yields a 2-pass streaming $(1+\epsilon)$-approximation for both problems; the algorithm uses $\tilde{O}(n^{1-\delta})$ space, for graphs of average degree $n^\delta$.

Includes members:
  Member "correlation clustering in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "sub exponential approximation schemes for csps from dense to almost sparse":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective")
  Member "sketching cuts in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "the sketching complexity of graph cuts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparsification of two variable valued csps")
  Member "streaming lower bounds for approximating max cut":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smaller cuts higher lower bounds")
  Member "better bounds for matchings in the streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "a note on no need to choose how to get both a ptas and sublinear query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "correlation clustering in data streams")
  Member "single pass sparsification in the streaming model with edge deletions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model")

Question: Please tell me which class the source paper 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)