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 "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"):
  Abstract: We consider the problem of maintaining an approximately maximum (fractional) matching and an approximately minimum vertex cover in a dynamic graph. Starting with the seminal paper by Onak and Rubinfeld [STOC 2010], this problem has received significant attention in recent years. There remains, however, a polynomial gap between the best known worst case update time and the best known amortised update time for this problem, even after allowing for randomisation. Specifically, Bernstein and Stein [ICALP 2015, SODA 2016] have the best known worst case update time. They present a deterministic data structure with approximation ratio $(3/2+\epsilon)$ and worst case update time $O(m^{1/4}/\epsilon^2)$, where $m$ is the number of edges in the graph. In recent past, Gupta and Peng [FOCS 2013] gave a deterministic data structure with approximation ratio $(1+\epsilon)$ and worst case update time $O(\sqrt{m}/\epsilon^2)$. No known randomised data structure beats the worst case update times of these two results. In contrast, the paper by Onak and Rubinfeld [STOC 2010] gave a randomised data structure with approximation ratio $O(1)$ and amortised update time $O(\log^2 n)$, where $n$ is the number of nodes in the graph. This was later improved by Baswana, Gupta and Sen [FOCS 2011] and Solomon [FOCS 2016], leading to a randomised date structure with approximation ratio $2$ and amortised update time $O(1)$. #R##N#We bridge the polynomial gap between the worst case and amortised update times for this problem, without using any randomisation. We present a deterministic data structure with approximation ratio $(2+\epsilon)$ and worst case update time $O(\log^3 n)$, for all sufficiently small constants $\epsilon$.

Includes members:
  Member "fully dynamic maximal matching in constant update time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "new deterministic approximation algorithms for fully dynamic matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "design of dynamic algorithms via primal dual method":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an improved algorithm for dynamic set cover")
  Member "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "fully dynamic matching in bipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating multistage matching problems")
  Member "popular conjectures imply strong lower bounds for dynamic problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "deterministic fully dynamic data structures for vertex cover and matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "higher lower bounds from the 3sum conjecture":
    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)