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 "tight bounds for online edge coloring"):
  Abstract: Vizing's celebrated theorem asserts that any graph of maximum degree $\Delta$ admits an edge coloring using at most $\Delta+1$ colors. In contrast, Bar-Noy, Naor and Motwani showed over a quarter century that the trivial greedy algorithm, which uses $2\Delta-1$ colors, is optimal among online algorithms. Their lower bound has a caveat, however: it only applies to low-degree graphs, with $\Delta=O(\log n)$, and they conjectured the existence of online algorithms using $\Delta(1+o(1))$ colors for $\Delta=\omega(\log n)$. Progress towards resolving this conjecture was only made under stochastic arrivals (Aggarwal et al., FOCS'03 and Bahmani et al., SODA'10). #R##N#We resolve the above conjecture for \emph{adversarial} vertex arrivals in bipartite graphs, for which we present a $(1+o(1))\Delta$-edge-coloring algorithm for $\Delta=\omega(\log n)$ known a priori. Surprisingly, if $\Delta$ is not known ahead of time, we show that no $\big(\frac{e}{e-1} - \Omega(1) \big) \Delta$-edge-coloring algorithm exists. We then provide an optimal, $\big(\frac{e}{e-1}+o(1)\big)\Delta$-edge-coloring algorithm for unknown $\Delta=\omega(\log n)$. Key to our results, and of possible independent interest, is a novel fractional relaxation for edge coloring, for which we present optimal fractional online algorithms and a near-lossless online rounding scheme, yielding our optimal randomized algorithms.

Includes members:
  Member "online matching with general arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "tight competitive ratios of classic matching algorithms in the fully online model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "how to match when all vertices arrive online":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "an economic based analysis of ranking for online bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "tighter bounds for online bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online matching with general arrivals")
  Member "deterministic distributed edge coloring with fewer colors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "dynamic algorithms for graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "deterministic distributed edge coloring via hypergraph maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed maximum matching verification in congest")

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)