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: "decomposing collectives for exploiting multi lane communication"
  Abstract: Many modern, high-performance systems increase the cumulated node-bandwidth by offering more than a single communication network and/or by having multiple connections to the network. Efficient algorithms and implementations for collective operations as found in, e.g., MPI must be explicitly designed for such multi-lane capabilities. We discuss a model for the design of multi-lane algorithms, and in particular give a recipe for converting any standard, one-ported, (pipelined) communication tree algorithm into a multi-lane algorithm that can effectively use $k$ lanes simultaneously. #R##N#We first examine the problem from the perspective of \emph{self-consistent performance guidelines}, and give simple, \emph{full-lane, mock-up implementations} of the MPI broadcast, reduction, gather, scatter, allgather, and alltoall operations using only similar operations of the given MPI library itself. The mock-up implementations, contrary to expectation, in many cases show surprising performance improvements with different MPI libraries on a small 36-node dual-socket, dual-lane Intel OmniPath cluster, indicating severe problems with the native MPI library implementations. Our full-lane implementations are in many cases considerably more than a factor of two faster than the corresponding MPI collectives. We see similar results on the larger Vienna Scientific Cluster, VSC-3. These experiments indicate considerable room for improvement of the MPI collectives in current libraries including more efficient use of multi-lane communication.

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.DC(Distributed, Parallel, and Cluster Computing)