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 "graph sketching against adaptive adversaries applied to the minimum degree algorithm"):
  Abstract: Motivated by the study of matrix elimination orderings in combinatorial scientific computing, we utilize graph sketching and local sampling to give a data structure that provides access to approximate fill degrees of a matrix undergoing elimination in $O(\text{polylog}(n))$ time per elimination and query. We then study the problem of using this data structure in the minimum degree algorithm, which is a widely-used heuristic for producing elimination orderings for sparse matrices by repeatedly eliminating the vertex with (approximate) minimum fill degree. This leads to a nearly-linear time algorithm for generating approximate greedy minimum degree orderings. Despite extensive studies of algorithms for elimination orderings in combinatorial scientific computing, our result is the first rigorous incorporation of randomized tools in this setting, as well as the first nearly-linear time algorithm for producing elimination orderings with provable approximation guarantees. #R##N#While our sketching data structure readily works in the oblivious adversary model, by repeatedly querying and greedily updating itself, it enters the adaptive adversarial model where the underlying sketches become prone to failure due to dependency issues with their internal randomness. We show how to use an additional sampling procedure to circumvent this problem and to create an independent access sequence. Our technique for decorrelating the interleaved queries and updates to this randomized data structure may be of independent interest.

Includes members:
  Member "optimal lower bounds for universal relation and for samplers and finding duplicates in streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "dynamic minimum spanning forest with subpolynomial worst case update time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "fully dynamic maximal matching in constant update time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "a framework for analyzing resparsification algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "fully dynamic all pairs shortest paths with worst case update time revisited":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate distance oracles subject to multiple vertex failures")
  Member "minimum fill in inapproximability and almost tight lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a fast minimum degree algorithm and matching lower bound")
  Member "fully dynamic spanners with worst case update time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph spanners a tutorial review")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

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)