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: "tuning mpi collectives by verifying performance guidelines"
  Abstract: MPI collective operations provide a standardized interface for performing data movements within a group of processes. The efficiency of collective communication operations depends on the actual algorithm, its implementation, and the specific communication problem (type of communication, message size, number of processes). Many MPI libraries provide numerous algorithms for specific collective operations. The strategy for selecting an efficient algorithm is often times predefined (hard-coded) in MPI libraries, but some of them, such as Open MPI, allow users to change the algorithm manually. Finding the best algorithm for each case is a hard problem, and several approaches to tune these algorithmic parameters have been proposed. We use an orthogonal approach to the parameter-tuning of MPI collectives, that is, instead of testing individual algorithmic choices provided by an MPI library, we compare the latency of a specific MPI collective operation to the latency of semantically equivalent functions, which we call the mock-up implementations. The structure of the mock-up implementations is defined by self-consistent performance guidelines. The advantage of this approach is that tuning using mock-up implementations is always possible, whether or not an MPI library allows users to select a specific algorithm at run-time. We implement this concept in a library called PGMPITuneLib, which is layered between the user code and the actual MPI implementation. This library selects the best-performing algorithmic pattern of an MPI collective by intercepting MPI calls and redirecting them to our mock-up implementations. Experimental results show that PGMPITuneLib can significantly reduce the latency of MPI collectives, and also equally important, that it can help identifying the tuning potential of MPI libraries.

Connected through hyperedge HE-1 (induced by source paper "decomposing collectives for exploiting multi lane communication"):
  with members:
    - "node aware improvements to allreduce"

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)