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: "high level programming abstractions for distributed graph processing"
  Abstract: Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

Connected through hyperedge HE-1 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "nxgraph an efficient graph processing system on a single machine"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-3 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  with members:
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "active access a mechanism for high performance distributed data centric computations"
    - "network accelerated non contiguous memory transfers"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "formalising opencypher graph queries in relational algebra"

Connected through hyperedge HE-4 (induced by source paper "graph processing on fpgas taxonomy survey challenges"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "an efficient dispatcher for large scale graphprocessing on opencl based fpgas"
    - "an efficient graph accelerator with parallel data conflict management"
    - "mathematical foundations of the graphblas"
    - "nxgraph an efficient graph processing system on a single machine"

Connected through hyperedge HE-5 (induced by source paper "distributed graphs in search of fast low latency resource efficient semantics rich big data processing"):
  with members:
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "ringo interactive graph analytics on big memory machines"

Connected through hyperedge HE-6 (induced by source paper "graphmp i o efficient big graph analytics on a single commodity machine"):
  with members:
    - "graphh high performance big graph analytics in small clusters"
    - "graphmp an efficient semi external memory big graph processing system on a single machine"
    - "gunrock a high performance graph processing library on the gpu"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-7 (induced by source paper "drone a distributed subgraph centric framework for processing large scale power law graphs"):
  with members:
    - "distributed edge partitioning for graph processing"
    - "goffish a sub graph centric framework for large scale graph analytics"

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)