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 "weighted matchings via unweighted augmentations"):
  Abstract: We design a generic method for reducing the task of finding weighted matchings to that of finding short augmenting paths in unweighted graphs. This method enables us to provide efficient implementations for approximating weighted matchings in the streaming model and in the massively parallel computation (MPC) model. #R##N#In the context of streaming with random edge arrivals, our techniques yield a $(1/2+c)$-approximation algorithm thus breaking the natural barrier of $1/2$. For multi-pass streaming and the MPC model, we show that any algorithm computing a $(1-\delta)$-approximate unweighted matching in bipartite graphs can be translated into an algorithm that computes a $(1-\varepsilon(\delta))$-approximate maximum weighted matching. Furthermore, this translation incurs only a constant factor (that depends on $\varepsilon> 0$) overhead in the complexity. Instantiating this with the current best multi-pass streaming and MPC algorithms for unweighted matchings yields the following results for maximum weighted matchings: #R##N#* A $(1-\varepsilon)$-approximation streaming algorithm that uses $O_\varepsilon(1)$ passes and $O_\varepsilon(n\, \text{poly} (\log n))$ memory. This is the first $(1-\varepsilon)$-approximation streaming algorithm for weighted matchings that uses a constant number of passes (only depending on $\varepsilon$). #R##N#* A $(1 - \varepsilon)$-approximation algorithm in the MPC model that uses $O_\varepsilon(\log \log n)$ rounds, $O(m/n)$ machines per round, and $O_\varepsilon(n\, \text{poly}(\log n))$ memory per machine. This improves upon the previous best approximation guarantee of $(1/2-\varepsilon)$ for weighted graphs.

Includes members:
  Member "exponentially faster massively parallel maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "matching and mis for uniformly sparse graphs in the low memory mpc model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees")
  Member "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "brief announcement semi mapreduce meets congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space")
  Member "massively parallel symmetry breaking on sparse graphs mis and maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity")
  Member "round compression for parallel graph algorithms in strongly sublinear space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "greedy and local ratio algorithms in the mapreduce model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)