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 "nested convex bodies are chaseable"):
  Abstract: In the Convex Body Chasing problem, we are given an initial point $v_0$ in $R^d$ and an online sequence of $n$ convex bodies $F_1, ..., F_n$. When we receive $F_i$, we are required to move inside $F_i$. Our goal is to minimize the total distance travelled. This fundamental online problem was first studied by Friedman and Linial (DCG 1993). They proved an $\Omega(\sqrt{d})$ lower bound on the competitive ratio, and conjectured that a competitive ratio depending only on d is possible. However, despite much interest in the problem, the conjecture remains wide open. #R##N#We consider the setting in which the convex bodies are nested: $F_1 \supset ... \supset F_n$. The nested setting is closely related to extending the online LP framework of Buchbinder and Naor (ESA 2005) to arbitrary linear constraints. Moreover, this setting retains much of the difficulty of the general setting and captures an essential obstacle in resolving Friedman and Linial's conjecture. In this work, we give the first $f(d)$-competitive algorithm for chasing nested convex bodies in $R^d$.

Includes members:
  Member "chasing convex bodies optimally":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "chasing convex bodies with linear competitive ratio":
    also connected through other hyperedges:
      (none)
  Member "a nearly linear bound for chasing nested convex bodies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "path length bounds for gradient descent and flow")
  Member "competitively chasing convex bodies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "chasing nested convex bodies nearly optimally":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "path length bounds for gradient descent and flow")
  Member "a tale of two metrics simultaneous bounds on competitiveness and regret":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")
  Member "the generalized work function algorithm is competitive for the generalized 2 server problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the online min sum set cover problem")
  Member "ramsey type theorems for metric spaces with applications to online problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")

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)