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 "kernelization via sampling with applications to dynamic graph streams"):
  Abstract: In this paper we present a simple but powerful subgraph sampling primitive that is applicable in a variety of computational models including dynamic graph streams (where the input graph is defined by a sequence of edge/hyperedge insertions and deletions) and distributed systems such as MapReduce. In the case of dynamic graph streams, we use this primitive to prove the following results:#R##N##R##N#* Matching: Our main result for matchings is that there exists an O~(k2) space algorithm that returns the edges of a maximum matching on the assumption the cardinality is at most k. The best previous algorithm used O~(kn) space where n is the number of vertices in the graph and we prove our result is optimal up to logarithmic factors. Our algorithm has O~(1) update time. We also show that there exists an O~(n2/α3) space algorithm that returns an α-approximation for matchings of arbitrary size. In independent work, Assadi et al. (arXiv 2015) proved this is optimal and provided an alternative algorithm. We generalize our exact and approximate algorithms to weighted matching. While there has been a substantial amount of work on approximate matching in insert-only graph streams, these are the first non-trivial results in the dynamic setting.#R##N##R##N#* Vertex Cover and Hitting Set: There exists an O~(kd) space algorithm that solves the minimum hitting set problem where d is the cardinality of the input sets and k is an upper bound on the size of the minimum hitting set. We prove this is optimal up to logarithmic factors. Our algorithm has O~(1) update time. The case d=2 corresponds to minimum vertex cover.#R##N##R##N#Finally, we consider a larger family of parameterized problems (including b-matching, disjoint paths, vertex coloring among others) for which our subgraph sampling primitive yields fast, small-space dynamic graph stream algorithms. We then show lower bounds for natural problems outside this family.

Includes members:
  Member "correlation clustering in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "tight bounds for linear sketches of approximate matchings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the sketching complexity of graph and hypergraph counting")
  Member "sublinear estimation of weighted matchings in dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "maximum matching in turnstile streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "applications of uniform sampling densest subgraph and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed dense subgraph detection and low outdegree orientation")
  Member "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "sketching cuts in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")

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)