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 "distributed graphs in search of fast low latency resource efficient semantics rich big data processing"):
  Abstract: Large graphs can be processed with single high-memory or distributed systems, focusing on querying the graph or executing algorithms using high-level APIs. For systems focused on processing graphs, common use-cases consist in executing algorithms such as PageRank or community detection on top of distributed systems that read from storage (local or distributed), compute and output results to storage in a way akin to a read-eval-write loop. #R##N#Graph analysis tasks face new hurdles with the additional dimension of evolving data. The systems we detail herein have considered the evolution of data in the form of stream processing. With it, semantics are offered to allow results' aggregation in windows which can be based on element counts or different time definitions. However, this semantic has yet to be incorporated in the expressiveness of graph processing itself. #R##N#We firstly detail the existing types of current graph analysis tasks; secondly, highlight state-of-the-art solutions for different aspects of these tasks. The resulting analysis identifies the need for systems to be able to effectively extend the aforementioned type of read-eval loop execution, by maintaining a graph (or parts of) in a cluster's memory for reuse, skipping the recurring I/O overhead which is present in all systems.

Includes members:
  Member "high level programming abstractions for distributed graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "the ubiquity of large graphs and surprising challenges of graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries")
  Member "ringo interactive graph analytics on big memory machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive low level storage of very large knowledge graphs")

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)