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: "parallel clique counting and peeling algorithms"
  Abstract: Dense subgraphs capture strong communities in social networks and entities possessing strong interactions in biological networks. In particular, $k$-clique counting and listing have applications in identifying important actors in a graph. However, finding $k$-cliques is computationally expensive, and thus it is important to have fast parallel algorithms. #R##N#We present a new parallel algorithm for $k$-clique counting that has polylogarithmic span and is work-efficient with respect to the well-known sequential algorithm for $k$-clique listing by Chiba and Nishizeki. Our algorithm can be extended to support listing and enumeration, and is based on computing low out-degree orientations. We present a new linear-work and polylogarithmic span algorithm for computing such orientations, and new parallel algorithms for producing unbiased estimations of clique counts. Finally, we design new parallel work-efficient algorithms for approximating the $k$-clique densest subgraph. Our first algorithm gives a $1/k$-approximation and is based on iteratively peeling vertices with the lowest clique counts; our algorithm is work-efficient, but we prove that this process is P-complete and hence does not have polylogarithmic span. Our second algorithm gives a $1/(k(1+\epsilon))$-approximation, is work-efficient, and has polylogarithmic span. #R##N#In addition, we implement these algorithms and propose optimizations. On a 60-core machine, we achieve 13.23-38.99x and 1.19-13.76x self-relative parallel speedup for $k$-clique counting and $k$-clique densest subgraph, respectively. Compared to the state-of-the-art parallel $k$-clique counting algorithms, we achieve a 1.31-9.88x speedup, and compared to existing implementations of $k$-clique densest subgraph, we achieve a 1.01-11.83x speedup. We are able to compute the $4$-clique counts on the largest publicly-available graph with over two hundred billion edges.

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)