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 "randomization can be as helpful as a glimpse of the future in online computation"):
  Abstract: We provide simple but surprisingly useful direct product theorems for proving lower bounds on online algorithms with a limited amount of advice about the future. As a consequence, we are able to translate decades of research on randomized online algorithms to the advice complexity model. Doing so improves significantly on the previous best advice complexity lower bounds for many online problems, or provides the first known lower bounds. For example, if $n$ is the number of requests, we show that: #R##N#(1) A paging algorithm needs $\Omega(n)$ bits of advice to achieve a competitive ratio better than $H_k=\Omega(\log k)$, where $k$ is the cache size. Previously, it was only known that $\Omega(n)$ bits of advice were necessary to achieve a constant competitive ratio smaller than $5/4$. #R##N#(2) Every $O(n^{1-\varepsilon})$-competitive vertex coloring algorithm must use $\Omega(n\log n)$ bits of advice. Previously, it was only known that $\Omega(n\log n)$ bits of advice were necessary to be optimal. #R##N#For certain online problems, including the MTS, $k$-server, paging, list update, and dynamic binary search tree problem, our results imply that randomization and sublinear advice are equally powerful (if the underlying metric space or node set is finite). This means that several long-standing open questions regarding randomized online algorithms can be equivalently stated as questions regarding online algorithms with sublinear advice. For example, we show that there exists a deterministic $O(\log k)$-competitive $k$-server algorithm with advice complexity $o(n)$ if and only if there exists a randomized $O(\log k)$-competitive $k$-server algorithm without advice. #R##N#Technically, our main direct product theorem is obtained by extending an information theoretical lower bound technique due to Emek, Fraigniaud, Korman, and Ros\'en [ICALP'09].

Includes members:
  Member "on the power of advice and randomization for online bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online bin covering with advice")
  Member "optimal online edge coloring of planar graphs with advice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tight bounds for online edge coloring")
  Member "online multi coloring with advice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight lower bounds for the complexity of multicoloring")
  Member "on the list update problem with advice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel online algorithms for the bin packing problem")
  Member "online makespan minimization with parallel schedules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online scheduling with makespan minimization state of the art results research challenges and open problems")
  Member "a competitive ratio approximation scheme for the k server problem in fixed finite metrics":
    also connected through other hyperedges:
      (none)
  Member "on advice complexity of the k server problem under sparse metrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advice complexity of the online search problem")
  Member "a new approach to online scheduling approximating the optimal competitive ratio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a competitive ratio approximation scheme for the k server problem in fixed finite metrics")

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)