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: "optimal online edge coloring of planar graphs with advice"
  Abstract: Using the framework of advice complexity, we study the amount of knowledge about the future that an online algorithm needs to color the edges of a graph optimally, i.e., using as few colors as possible. For graphs of maximum degree $\Delta$, it follows from Vizing's Theorem that $O(m\log \Delta)$ bits of advice suffice to achieve optimality, where $m$ is the number of edges. We show that for graphs of bounded degeneracy (a class of graphs including e.g. trees and planar graphs), only $O(m)$ bits of advice are needed to compute an optimal solution online, independently of how large $\Delta$ is. On the other hand, we show that $\Omega (m)$ bits of advice are necessary just to achieve a competitive ratio better than that of the best deterministic online algorithm without advice. Furthermore, we consider algorithms which use a fixed number of advice bits per edge (our algorithm for graphs of bounded degeneracy belongs to this class of algorithms). We show that for bipartite graphs, any such algorithm must use at least $\Omega(m\log \Delta)$ bits of advice to achieve optimality.

Connected through hyperedge HE-1 (induced by source paper "tight bounds for online edge coloring"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "deterministic distributed edge coloring with fewer colors"
    - "dynamic algorithms for graph coloring"
    - "deterministic distributed edge coloring via hypergraph maximal matching"

Connected through hyperedge HE-2 (induced by source paper "randomization can be as helpful as a glimpse of the future in online computation"):
  with members:
    - "on the power of advice and randomization for online bipartite matching"
    - "online multi coloring with advice"
    - "on the list update problem with advice"
    - "online makespan minimization with parallel schedules"
    - "a competitive ratio approximation scheme for the k server problem in fixed finite metrics"
    - "on advice complexity of the k server problem under sparse metrics"
    - "a new approach to online scheduling approximating the optimal competitive ratio"
    - "online bin packing with advice"

Connected through hyperedge HE-3 (induced by source paper "the advice complexity of a class of hard online problems"):
  with members:
    - "online algorithms with advice for bin packing and scheduling problems"
    - "on the list update problem with advice"
    - "on advice complexity of the k server problem under sparse metrics"
    - "online bin packing with advice"

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)