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: "partitionedvc partitioned external memory graph analytics framework for ssds"
  Abstract: Graphs analytics are at the heart of a broad range of applications such as drug discovery, page ranking, transportation systems, and recommendation systems. When graph size exceeds memory size, out-of-core graph processing is needed. For the widely used external memory graph processing systems, accessing storage becomes the bottleneck. We make the observation that nearly all graph algorithms have a dynamically varying number of active vertices that must be processed in each iteration. However, existing graph processing frameworks, such as GraphChi, load the entire graph in each iteration even if a small fraction of the graph is active. This limitation is due to the structure of the data storage used by these systems. In this work, we propose to use a compressed sparse row (CSR) based graph storage that is more amenable for selectively loading only a few active vertices in each iteration. But CSR based graph processing suffers from random update propagation to many target vertices. To solve this challenge we propose to use a multi-log update mechanism which logs updates separately, rather than directly update the active edge in a graph. Our proposed multi-log system maintains a separate log per each vertex interval. This separation enables us to efficiently process each vertex interval by just loading the corresponding log. Over the current state of the art out-of-core graph processing framework, our evaluation results show that the PartitionedVC framework improves performance by up to $16.40\times$, $1.13\times$, $1.64\times$, $1.38\times$, and $2.76\times$ for the widely used breadth-first search, pagerank, community detection, graph coloring, and the maximal independent set applications, respectively.

Connected through hyperedge HE-1 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  with members:
    - "graphit a high performance dsl for graph analytics"
    - "accelerating pagerank using partition centric processing"
    - "parallel local graph clustering"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)