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: "a structure aware approach for efficient graph processing"
  Abstract: With the advent of the big data, graph are processed in an iterative manner, which incrementally described in the form of graph in big data applications. Most currently, graph processing methods treat the underlying map data as black boxes. However, as shown in experimental evaluation, graph structures often have diversity, different graph processing methods are very sensitive to the graph structure and show different performance for different data sets. Based on this, a graph processing method for graph structure analysis is proposed in this paper: (1) This paper calculates the vertex activity of a graph according to the in-degree and out-degree, and divide the corresponding vertices into the hot or cold partitions; (2) According to the change of graph structure caused by partial vertex convergence after iteration, this paper reclassifies the partitions, divides the lower active vertices into cold partition and reduces the frequency of calculation, which thereby reducing the cache miss rate and the I/O overhead caused by active vertices as well; (3) The partition with highest vertex status degree are given a priority calculation in this paper. In detail, more pronounced and more frequent vertices have higher processing priority. In this way, the convergence speed of the graph vertices is accelerated, and the running time of the graph algorithm in the big data environment is reduced. Our experiments show that compared with the latest system, the proposed method can double the performance of different graph algorithms and data sets.

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)