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 "amortized dynamic cell probe lower bounds from four party communication"):
  Abstract: This paper develops a new technique for proving amortized, randomized cell-probe lower bounds on dynamic data structure problems. We introduce a new randomized nondeterministic four-party communication model that enables "accelerated", error-preserving simulations of dynamic data structures. #R##N#We use this technique to prove an $\Omega(n(\log n/\log\log n)^2)$ cell-probe lower bound for the dynamic 2D weighted orthogonal range counting problem (2D-ORC) with $n/\mathrm{poly}\log n$ updates and $n$ queries, that holds even for data structures with $\exp(-\tilde{\Omega}(n))$ success probability. This result not only proves the highest amortized lower bound to date, but is also tight in the strongest possible sense, as a matching upper bound can be obtained by a deterministic data structure with worst-case operational time. This is the first demonstration of a "sharp threshold" phenomenon for dynamic data structures. #R##N#Our broader motivation is that cell-probe lower bounds for exponentially small success facilitate reductions from dynamic to static data structures. As a proof-of-concept, we show that a slightly strengthened version of our lower bound would imply an $\Omega((\log n /\log\log n)^2)$ lower bound for the static 3D-ORC problem with $O(n\log^{O(1)}n)$ space. Such result would give a near quadratic improvement over the highest known static cell-probe lower bound, and break the long standing $\Omega(\log n)$ barrier for static data structures.

Includes members:
  Member "cell probe lower bounds for dynamic problems via a new communication model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new amortized cell probe lower bounds for dynamic problems")
  Member "new unconditional hardness results for dynamic and online problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic matrix inverse improved algorithms and matching conditional lower bounds")
  Member "cell probe bounds for online edit distance and other pattern matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new unconditional hardness results for dynamic and online problems")
  Member "on arthur merlin games in communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vanishing error approximate degree and qma complexity")
  Member "the cell probe complexity of dynamic range counting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "lower bounds on near neighbor search via metric expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "logarithmic lower bounds in the cell probe model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coarse grained complexity for dynamic algorithms")

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)