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: "fusible hsts and the randomized k server conjecture"
  Abstract: We show that a potential-based algorithm for the fractional $k$-server problem on hierarchically separated trees (HSTs) with competitive ratio $f(k)$ can be used to obtain a randomized algorithm for any metric space with competitive ratio $f(k)^2 O((\log k)^2)$. Employing the $O((\log k)^2)$-competitive algorithm for HSTs from our joint work with Bubeck, Cohen, Lee, and M\k{a}dry (2017), this yields an $O((\log k)^6)$-competitive algorithm for the $k$-server problem on general metric spaces. #R##N#The best previous result independent of the geometry of the underlying metric space is the $2k-1$ competitive ratio established for the deterministic work function algorithm by Koutsoupias and Papadimitriou (1995). Even for the special case when the underlying metric space is the real line, the best known competitive ratio was $k$. Since deterministic algorithms can do no better than $k$ on any metric space with at least $k+1$ points, this establishes that for every metric space on which the problem is non-trivial, randomized algorithms give an exponential improvement over deterministic algorithms.

Connected through hyperedge HE-1 (induced by source paper "understand dynamic regret with switching cost for online decision making"):
  with members:
    - "introduction to online convex optimization"
    - "proximal online gradient is optimum for dynamic regret"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "k server via multiscale entropic regularization"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-2 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation"):
  with members:
    - "shared autonomous vehicle simulation and service design"
    - "dynamic trip vehicle dispatch with scheduled and on demand requests"
    - "optimization of ride sharing systems using event driven receding horizon control"
    - "anytime heuristic for weighted matching through altruism inspired behavior"
    - "the online k taxi problem"
    - "a primal dual online deterministic algorithm for matching with delays"
    - "spatio temporal pricing for ridesharing platforms"
    - "stochastic k server how should uber work"

Connected through hyperedge HE-3 (induced by source paper "unbounded lower bound for k server against weak adversaries"):
  with members:
    - "the infinite server problem"
    - "the h k server problem on bounded depth trees"
    - "ramsey type theorems for metric spaces with applications to online problems"
    - "the k server dual and loose competitiveness for paging"
    - "competitive paging algorithms"

Connected through hyperedge HE-4 (induced by source paper "managing multiple mobile resources"):
  with members:
    - "the mobile server problem"
    - "dynamic beats fixed on phase based algorithms for file migration"
    - "competitive paging algorithms"

Connected through hyperedge HE-5 (induced by source paper "competitively chasing convex bodies"):
  with members:
    - "introduction to online convex optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "k server via multiscale entropic regularization"
    - "nested convex bodies are chaseable"

Connected through hyperedge HE-6 (induced by source paper "slaying hydrae improved bounds for generalized k server in uniform metrics"):
  with members:
    - "weighted k server bounds via combinatorial dichotomies"
    - "competitive algorithms for generalized k server in uniform metrics"
    - "on randomized memoryless algorithms for the weighted k server problem"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "ramsey type theorems for metric spaces with applications to online problems"

Connected through hyperedge HE-7 (induced by source paper "the online k taxi problem"):
  with members:
    - "weighted k server bounds via combinatorial dichotomies"
    - "the h k server problem on bounded depth trees"

Connected through hyperedge HE-8 (induced by source paper "multi finger binary search trees"):
  with members:
    - "in pursuit of the dynamic optimality conjecture"
    - "on dynamic optimality for binary search trees"

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)