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: "bladyg a graph processing framework for large dynamic graphs"
  Abstract: Abstract   Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, PowerGraph, GraphLab, and Trinity. However, these systems deal only with static graphs and do not consider the issue of processing evolving and dynamic graphs. In this paper, we are considering the issues of scale and dynamism in the case of graph processing systems. We present  bladyg , a graph processing framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of  bladyg  on top of  akka  framework. We experimentally evaluate the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs. The experimental results show that the performance and scalability of  bladyg  are satisfying for large-scale dynamic graphs.

Connected through hyperedge HE-1 (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-2 (induced by source paper "an experimental survey on big data frameworks"):
  with members:
    - "mr simlab scalable subgraph selection with label similarity for big data"
    - "big graph mining frameworks and techniques"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-3 (induced by source paper "big data frameworks a comparative study"):
  with members:
    - "mr simlab scalable subgraph selection with label similarity for big data"
    - "big graph mining frameworks and techniques"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

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)