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 "elastic partition placement for non stationary graph algorithms"):
  Abstract: Distributed graph platforms like Pregel have usedvertex-centric programming models to process the growing cor-pus of graph datasets using commodity clusters. However, theirregular structure of graphs causes load imbalances acrossmachines, and this is exacerbated for non-stationary graphalgorithms where not all parts of the graph are active at thesame time. As a result, such graph platforms do not make efficientuse of distributed resources. In this paper, we decouple graphpartitioning from placement on hosts, and introduce strategiesfor elastic placement of graph partitions on Cloud VMs to reducethe cost of execution compared to a static placement, even aswe minimize the increase in makespan. These strategies areinnovative in modeling the graph algorithm's non-stationarybehavior a priori using a metagraph sketch. We validate ourstrategies for several real-world graphs, using runtime tracesfor approximate Betweenness Centrality (BC) algorithm on oursubgraph-centric GoFFish graph platform. Our strategies areable to reduce the cost of execution by up to 54%, comparedto a static placement, while achieving a makespan that is within25% of the optimal.

Includes members:
  Member "partitioning strategies for load balancing subgraph centric distributed graph processing":
    also connected through other hyperedges:
      (none)
  Member "distributed edge partitioning for graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "drone a distributed subgraph centric framework for processing large scale power law graphs")
  Member "goffish a sub graph centric framework for large scale graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a distributed path query engine for temporal property graphs")
  Member "identifying high betweenness centrality nodes in large social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach")

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)