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 "low latency graph streaming using compressed purely functional trees"):
  Abstract: Due to the dynamic nature of real-world graphs, there has been a growing interest in the graph-streaming setting where a continuous stream of graph updates is mixed with arbitrary graph queries. In principle, purely-functional trees are an ideal choice for this setting due as they enable safe parallelism, lightweight snapshots, and strict serializability for queries. However, directly using them for graph processing would lead to significant space overhead and poor cache locality. #R##N#This paper presents C-trees, a compressed purely-functional search tree data structure that significantly improves on the space usage and locality of purely-functional trees. The key idea is to use a chunking technique over trees in order to store multiple entries per tree-node. We design theoretically-efficient and practical algorithms for performing batch updates to C-trees, and also show that we can store massive dynamic real-world graphs using only a few bytes per edge, thereby achieving space usage close to that of the best static graph processing frameworks. #R##N#To study the efficiency and applicability of our data structure, we designed Aspen, a graph-streaming framework that extends the interface of Ligra with operations for updating graphs. We show that Aspen is faster than two state-of-the-art graph-streaming systems, Stinger and LLAMA, while requiring less memory, and is competitive in performance with the state-of-the-art static graph frameworks, Galois, GAP, and Ligra+. With Aspen, we are able to efficiently process the largest publicly-available graph with over two hundred billion edges in the graph-streaming setting using a single commodity multicore server with 1TB of memory.

Includes members:
  Member "multiversion concurrency with bounded delay and precise garbage collection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "concurrent reference counting and resource management in wait free constant time")
  Member "theoretically efficient parallel graph algorithms can be fast and scalable":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "pam parallel augmented maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal parallel algorithms in the binary forking model")
  Member "parallel local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering")
  Member "an introduction to temporal graphs an algorithmic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph generators state of the art and open challenges")
  Member "storing and analyzing historical graph data at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "the gap benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus")
  Member "efficient snapshot retrieval over historical graph data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")

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)