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: "scalable rdf data compression using x10"
  Abstract: The Semantic Web comprises enormous volumes#R##N#of semi-structured data elements. For interoperability, these#R##N#elements are represented by long strings. Such representations#R##N#are not efficient for the purposes of Semantic Web applications#R##N#that perform computations over large volumes of information.#R##N#A typical method for alleviating the impact of this problem is#R##N#through the use of compression methods that produce more#R##N#compact representations of the data. The use of dictionary#R##N#encoding for this purpose is particularly prevalent in Semantic#R##N#Web database systems. However, centralized implementations#R##N#present performance bottlenecks, giving rise to the need for#R##N#scalable, efficient distributed encoding schemes. In this paper,#R##N#we describe an encoding implementation based on the asynchronous#R##N#partitioned global address space (APGAS) parallel#R##N#programming model. We evaluate performance on a cluster of#R##N#up to 384 cores and datasets of up to 11 billion triples (1.9#R##N#TB). Compared to the state-of-art MapReduce algorithm, we#R##N#demonstrate a speedup of 2:6 - 7:4X and excellent scalability.#R##N#These results illustrate the strong potential of the APGAS#R##N#model for efficient implementation of dictionary encoding and#R##N#contributes to the engineering of larger scale Semantic Web#R##N#applications.

Connected through hyperedge HE-1 (induced by source paper "a survey on methods and systems for graph compression"):
  with members:
    - "preach a fast lightweight reachability index using pruning and contraction hierarchies"
    - "graphchi db simple design for a scalable graph database system on just a pc"
    - "dagger a scalable index for reachability queries in large dynamic graphs"
    - "graphlab a new framework for parallel machine learning"

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)