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: "on advice complexity of the k server problem under sparse metrics"
  Abstract: We consider the k-server problem under the advice model of computation when the underlying metric space is sparse. On one side, we show that an advice of size {\Omega}(n) is required to obtain a 1-competitive algorithm for sequences of size n, even for the 2-server problem on a path metric of size N >= 5. Through another lower bound argument, we show that at least (n/2)(log {\alpha} - 1.22) bits of advice is required to obtain an optimal solution for metric spaces of treewidth {\alpha}, where 4 <= {\alpha} < 2k. On the other side, we introduce {\Theta}(1)-competitive algorithms for a wide range of sparse graphs, which require advice of (almost) linear size. Namely, we show that for graphs of size N and treewidth {\alpha}, there is an online algorithm which receives $O(n (log {\alpha} + log log N))$ bits of advice and optimally serves a sequence of length n. With a different argument, we show that if a graph admits a system of {\mu} collective tree (q,r)-spanners, then there is a (q+r)-competitive algorithm which receives O(n (log {\mu} + log log N)) bits of advice. Among other results, this gives a 3-competitive algorithm for planar graphs, provided with O(n log log N) bits of advice.

Connected through hyperedge HE-1 (induced by source paper "advice complexity of the online search problem"):
  with members:
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "a comparison of performance measures via online search"

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"
    - "optimal online edge coloring of planar graphs with advice"
    - "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"
    - "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 "online algorithms with advice for bin packing and scheduling problems"):
  with members:
    - "online bin packing with advice"

Connected through hyperedge HE-4 (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"
    - "optimal online edge coloring of planar graphs with advice"
    - "on the list update problem with advice"
    - "online bin packing with advice"

Connected through hyperedge HE-5 (induced by source paper "on the list update problem with advice"):
  with members:
    - "optimal lower bounds for projective list update algorithms"
    - "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)