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: "stochastic k server how should uber work"
  Abstract: In this paper, we study a stochastic variant of the celebrated k-server problem. In the k-server problem, we are required to minimize the total movement of k servers that are serving an online sequence of t requests in a metric. In the stochastic setting we are given t independent distributions   in advance, and at every time step i a request is drawn from Pi. Designing the optimal online algorithm in such setting is NP-hard, therefore the emphasis of our work is on designing an approximately optimal online algorithm. We first show a structural characterization for a certain class of non-adaptive online algorithms. We prove that in general metrics, the best of such algorithms has a cost of no worse than three times that of the optimal online algorithm. Next, we present an integer program that finds the optimal algorithm of this class for any arbitrary metric. Finally, by rounding the solution of the linear relaxation of this program, we present an online algorithm for the stochastic k-server problem with the approximation factor of 3 in the line and circle metrics and O(log n) in a general metric of size n. Moreover, we define the Uber problem, in which each demand consists of two endpoints, a source and a destination. We show that given an a-approximation algorithm for the k-server problem, we can obtain an (a+2)-approximation algorithm for the Uber problem. Motivated by the fact that demands are usually highly correlated with the time we study the stochastic Uber problem. Furthermore, we extend our results to the correlated setting where the probability of a request arriving at a certain point depends not only on the time step but also on the previously arrived requests.

Connected through hyperedge HE-1 (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"
    - "fusible hsts and the randomized k server conjecture"

Connected through hyperedge HE-2 (induced by source paper "stochastic online metric matching"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "online allocation with traffic spikes mixing adversarial and stochastic models"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "near optimum online ad allocation for targeted advertising"

Connected through hyperedge HE-3 (induced by source paper "prophets secretaries and maximizing the probability of choosing the best"):
  with members:
    - "prophet secretary for combinatorial auctions and matroids"
    - "online matroid intersection beating half for random arrival"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "mechanism design via correlation gap"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-4 (induced by source paper "dispatch an optimally competitive algorithm for maximum online perfect bipartite matching with i i d arrivals"):
  with members:
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"

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)