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: "don t rush into a union take time to find your roots"
  Abstract: We present a new threshold phenomenon in data structure lower bounds where slightly reduced update times lead to exploding query times. Consider incremental connectivity, letting t_u be the time to insert an edge and t_q be the query time. For t_u = Omega(t_q), the problem is equivalent to the well-understood union-find problem: InsertEdge(s,t) can be implemented by Union(Find(s), Find(t)). This gives worst-case time t_u = t_q = O(lg n / lglg n) and amortized t_u = t_q = O(alpha(n)). #R##N#By contrast, we show that if t_u = o(lg n / lglg n), the query time explodes to t_q >= n^{1-o(1)}. In other words, if the data structure doesn't have time to find the roots of each disjoint set (tree) during edge insertion, there is no effective way to organize the information! #R##N#For amortized complexity, we demonstrate a new inverse-Ackermann type trade-off in the regime t_u = o(t_q). #R##N#A similar lower bound is given for fully dynamic connectivity, where an update time of o(\lg n) forces the query time to be n^{1-o(1)}. This lower bound allows for amortization and Las Vegas randomization, and comes close to the known O(lg n * poly(lglg n)) upper bound.

Connected through hyperedge HE-1 (induced by source paper "new amortized cell probe lower bounds for dynamic problems"):
  with members:
    - "amortized dynamic cell probe lower bounds from four party communication"
    - "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"
    - "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-2 (induced by source paper "fully dynamic connectivity in o log n log log n 2 amortized expected time"):
  with members:
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "faster worst case deterministic dynamic connectivity"
    - "faster deterministic fully dynamic graph connectivity"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-3 (induced by source paper "deterministic worst case dynamic connectivity simpler and faster"):
  with members:
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "faster deterministic fully dynamic graph connectivity"
    - "logarithmic lower bounds in the cell probe model"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-4 (induced by source paper "dynamic set intersection"):
  with members:
    - "threesomes degenerates and love triangles"
    - "3sum hardness in dynamic data structures"
    - "four soviets walk the dog with an application to alt s conjecture"
    - "faster deterministic fully dynamic graph connectivity"
    - "the power of simple tabulation hashing"
    - "fast set intersection and two patterns matching"
    - "fast evaluation of union intersection expressions"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-5 (induced by source paper "the cell probe complexity of dynamic range counting"):
  with members:
    - "lower bounds on near neighbor search via metric expansion"
    - "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)