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: "gpu algorithm for earliest arrival time problem in public transport networks"
  Abstract: Given a temporal graph G, a source vertex s, and a departure time at source vertex t_s, the earliest arrival time problem EAT is to start from s on or after t_s and reach all the vertices in G as early as possible. Ni et al. have proposed a parallel algorithm for EAT and obtained a speedup up to 9.5 times on real-world graphs with respect to the connection-scan serial algorithm by using multi-core processors. #R##N#We propose a topology-driven parallel algorithm for EAT on public transport networks and implement using general-purpose programming on the graphics processing unit GPU. A temporal edge or connection in a temporal graph for a public transport network is associated with a departure time and a duration time, and many connections exist from u to v for an edge (u,v). We propose two pruning techniques connection-type and clustering, and use arithmetic progression technique appropriately to process many connections of an edge, without scanning all of them. In the connection-type technique, the connections of an edge with the same duration are grouped together. In the clustering technique, we follow 24-hour format and the connections of an edge are partitioned into 24 clusters so that the departure time of connections in the i^{th} cluster is at least i-hour and at most i+1-hour. The arithmetic progression technique helps to store a sequence of departure times of various connections in a compact way. We propose a hybrid approach to combine the three techniques connection-type, clustering and arithmetic progression in an appropriate way. Our techniques achieve an average speedup up to 59.09 times when compared to the existing connection-scan serial algorithm running on CPU. Also, the average speedup of our algorithm is 12.48 times against the parallel edge-scan-dependency graph algorithm running on GPU.

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)