human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  Abstract: Past decade has seen the development of many shared-memory graph processing frameworks intended to reduce the effort of developing high performance parallel applications. However, many of these frameworks, based on Vertex-centric or Edge-centric paradigms suffer from several issues such as poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate overall performance and scalability. #R##N#In this paper, we generalize a recent partition-centric paradigm for PageRank computation to a novel Graph Processing Over Partitions (GPOP) framework that exploits the locality of partitioning to dramatically improve the cache performance of a variety of graph algorithms. It achieves high scalability by enabling completely lock and atomic free computation. Its built-in analytical performance model enables it to use a hybrid of source and partition centric communication modes in a way that ensures work-efficiency each iteration while simultaneously boosting high bandwidth sequential memory accesses. Finally, the GPOP framework is designed with programmability in mind. It completely abstracts away underlying programming model details from the user and provides an easy to program set of APIs with the ability to selectively continue the active vertex set across iterations. #R##N#We extensively evaluate the performance of GPOP for a variety of graph algorithms, using several large datasets. We observe that GPOP incurs upto 8.6x and 5.2x less L2 cache misses compared to Ligra and GraphMat, respectively. In terms of execution time, GPOP is upto 19x and 6.1x faster than Ligra and GraphMat, respectively.

Includes members:
  Member "partitionedvc partitioned external memory graph analytics framework for ssds":
    also connected through other hyperedges:
      (none)
  Member "graphit a high performance dsl for graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new frontier for pull based graph processing")
  Member "accelerating pagerank using partition centric processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a graphblas approach for subgraph counting")
  Member "parallel local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering")
  Member "a work efficient parallel sparse matrix sparse vector multiplication algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fastsv a distributed memory connected component algorithm with fast convergence")
  Member "goffish a sub graph centric framework for large scale graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a distributed path query engine for temporal property graphs")
  Member "geotagging one hundred million twitter accounts with total variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "demographic inference and representative population estimates from multilingual social media data")
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")

Question: Please tell me which class the source paper 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)