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: "when hashing met matching efficient search for potential matches in ride sharing"
  Abstract: We study the problem of matching rides in a ride sharing platform. Such platforms face the daunting combinatorial task of finding potential matches for rides from a matching pool of tens of thousands of rides very efficiently while retaining near-optimality compared to an exhaustive search. We formalize this problem and present a novel algorithm for it based on the beautiful theory of locality sensitive hashing for Maximum Inner Product Search (MIPS). The proposed algorithm can find $k$ (can be practically a constant for ride sharing platforms) potential matches for a given ride from a pool of $n$ rides in sub-linear time $O(n^\rho (k + \log n) \log k)$ for $\rho < 1$, which is significant saving compared to an exhaustive search in the pool requiring $O(n)$ time. The space requirement for our algorithm is $O(n^{1 + \rho} \log k)$. We show that the set of $k$ potential matches include the near-optimal ones with high probability. Implementation of our algorithm could efficiently find near-optimal set of potential matches with high probability from a pool of thousands of real rides.

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)