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 "space efficient approximation to maximum matching size from uniform edge samples"):
  Abstract: Given a source of iid samples of edges of an input graph $G$ with $n$ vertices and $m$ edges, how many samples does one need to compute a constant factor approximation to the maximum matching size in $G$? Moreover, is it possible to obtain such an estimate in a small amount of space? We show that, on the one hand, this problem cannot be solved using a nontrivially sublinear (in $m$) number of samples: $m^{1-o(1)}$ samples are needed. On the other hand, a surprisingly space efficient algorithm for processing the samples exists: $O(\log^2 n)$ bits of space suffice to compute an estimate. #R##N#Our main technical tool is a new peeling type algorithm for matching that we simulate using a recursive sampling process that crucially ensures that local neighborhood information from `dense' regions of the graph is provided at appropriately higher sampling rates. We show that a delicate balance between exploration depth and sampling rate allows our simulation to not lose precision over a logarithmic number of levels of recursion and achieve a constant factor approximation. The previous best result on matching size estimation from random samples was a $\log^{O(1)} n$ approximation [Kapralov et al'14]. #R##N#Our algorithm also yields a constant factor approximate local computation algorithm (LCA) for matching with $O(d\log n)$ exploration starting from any vertex. Previous approaches were based on local simulations of randomized greedy, which take $O(d)$ time {\em in expectation over the starting vertex or edge} (Yoshida et al'09, Onak et al'12), and could not achieve a better than $d^2$ runtime. Interestingly, we also show that unlike our algorithm, the local simulation of randomized greedy that is the basis of the most efficient prior results does take $\wt{\Omega}(d^2)\gg O(d\log n)$ time for a worst case edge even for $d=\exp(\Theta(\sqrt{\log n}))$.

Includes members:
  Member "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "weighted matchings via unweighted augmentations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "extractor based time space lower bounds for learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "estimating graph parameters from random order streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "testable bounded degree graph properties are random order streamable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing")
  Member "on estimating maximum matching size in graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "a 2 ϵ approximation for maximum weight matching in the semi streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")

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)