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: "rapidly mixing markov chains a comparison of techniques a survey"
  Abstract: We survey existing techniques to bound the mixing time of Markov chains. The mixing time is related to a geometric parameter called conductance which is a measure of edge-expansion. Bounds on conductance are typically obtained by a technique called "canonical paths" where the idea is to find a set of paths, one between every source-destination pair, such that no edge is heavily congested. However, the canonical paths approach cannot always show rapid mixing of a rapidly mixing chain. This drawback disappears if we allow the flow between a pair of states to be spread along multiple paths. We prove that for a large class of Markov chains canonical paths does capture rapid mixing. Allowing multiple paths to route the flow still does help a great deal in proofs, as illustrated by a result of Morris & Sinclair (FOCS'99) on the rapid mixing of a Markov chain for sampling 0/1 knapsack solutions. #R##N#A different approach to prove rapid mixing is "Coupling". Path Coupling is a variant discovered by Bubley & Dyer (FOCS'97) that often tremendously reduces the complexity of designing good Couplings. We present several applications of Path Coupling in proofs of rapid mixing. These invariably lead to much better bounds on mixing time than known using conductance, and moreover Coupling based proofs are typically simpler. This motivates the question of whether Coupling can be made to work whenever the chain is rapidly mixing. This question was answered in the negative by Kumar & Ramesh (FOCS'99), who showed that no Coupling strategy can prove the rapid mixing of the Jerrum-Sinclair chain for sampling perfect and near-perfect matchings.

Connected through hyperedge HE-1 (induced by source paper "mix and match markov chains mixing times for matching in rideshare"):
  with members:
    - "minimizing latency in online ride and delivery services"
    - "spatio temporal pricing for ridesharing platforms"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "deep reinforcement learning an overview"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "an optimization framework for online ride sharing markets"

Connected through hyperedge HE-2 (induced by source paper "randomly coloring graphs of bounded treewidth"):
  with members:
    - "an fptas for counting proper four colorings on cubic graphs"
    - "random cluster dynamics for the ising model is rapidly mixing"
    - "sparsest cut on bounded treewidth graphs algorithms and hardness results"
    - "known algorithms on graphs of bounded treewidth are probably optimal"

Connected through hyperedge HE-3 (induced by source paper "multiplayer parallel repetition for expander games"):
  with members:
    - "parallel repetition simplification and the no signaling case"

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)