human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "tight static lower bounds for non adaptive data structures"):
  with members:
    - "static data structure lower bounds imply rigidity"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "set membership with non adaptive bit probes"
    - "set membership with a few bit probes"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "the power of simple tabulation hashing"
    - "linear probing with constant independence"

Connected through hyperedge HE-2 (induced by source paper "new amortized cell probe lower bounds for dynamic problems"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "cell probe lower bounds for dynamic problems via a new communication model"
    - "new unconditional hardness results for dynamic and online problems"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "don t rush into a union take time to find your roots"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for oblivious data structures"):
  with members:
    - "bios oram improved privacy preserving data access for parameterized outsourced storage"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "decreasekeys are expensive for external memory priority queues"
    - "cell probe lower bounds for dynamic problems via a new communication model"
    - "new unconditional hardness results for dynamic and online problems"
    - "the cell probe complexity of dynamic range counting"
    - "privacy preserving group data access via stateless oblivious ram simulation"
    - "lower bounds on near neighbor search via metric expansion"

Connected through hyperedge HE-4 (induced by source paper "tight cell probe bounds for succinct boolean matrix vector multiplication"):
  with members:
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "faster online matrix vector multiplication"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "new unconditional hardness results for dynamic and online problems"
    - "adapt or die polynomial lower bounds for non adaptive dynamic data structures"
    - "the cell probe complexity of dynamic range counting"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-5 (induced by source paper "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"):
  with members:
    - "new unconditional hardness results for dynamic and online problems"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "logarithmic lower bounds in the cell probe model"
    - "bounds for small error and zero error quantum algorithms"

Connected through hyperedge HE-6 (induced by source paper "cell probe lower bounds from online communication complexity"):
  with members:
    - "faster online matrix vector multiplication"
    - "cell probe lower bounds for dynamic problems via a new communication model"
    - "new unconditional hardness results for dynamic and online problems"
    - "logarithmic lower bounds in the cell probe model"

Question: Please tell me which class the center node 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)