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: "betweenness centrality is more parallelizable than dense matrix multiplication"
  Abstract: General infrastructure and scalable algorithms for sparse matrix multiplication enable succinct high-performance implementation of numerical methods and graph algorithms. We showcase the theoretical and practical quality of novel sparse matrix multiplication routines in Cyclops Tensor Framework (CTF) via MFBC: a Maximal Frontier Betweenness Centrality algorithm. Our sparse matrix multiplication algorithms and consequently MFBC perform asymptotically less communication than previous approaches. For graphs with $n$ vertices and average degree $k$, we show that on $p$ processors, MFBC performs a factor of $p^{1/3}$ less communication than known alternatives when $k=n/p^{2/3}$. If $p$ processors are needed to fit the problem in memory, all costs associated with the algorithm can be reduced by a factor of $s=(n/k)\sqrt{p}$ when using $sp$ processors. For multiplication of square dense matrices only $s=\sqrt{p}$ is achievable. #R##N#We formulate and implement MFBC for weighted graphs, by leveraging specially-designed monoids and functions. We prove the correctness of the new formulation. CTF allows a parallelism-oblivious C++ implementation of MFBC to achieve good scalability for both extremely sparse and relatively dense graphs. The library automatically searches a space of distributed data decompositions and sparse matrix multiplication algorithms. The resulting code outperforms the well-known CombBLAS library by factors of up to 8 and shows more robust performance. Our design methodology is general and readily extensible to other graph problems.

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)