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: "a distributed multilevel force directed algorithm"
  Abstract: The wide availability of powerful and inexpensive cloud computing services naturally motivates the study of distributed graph layout algorithms, able to scale to very large graphs. Nowadays, to process Big Data, companies are increasingly relying on PaaS infrastructures rather than buying and maintaining complex and expensive hardware. So far, only a few examples of basic force-directed algorithms that work in a distributed environment have been described. Instead, the design of a distributed multilevel force-directed algorithm is a much more challenging task, not yet addressed. We present the first multilevel force-directed algorithm based on a distributed vertex-centric paradigm, and its implementation on Giraph, a popular platform for distributed graph algorithms. Experiments show the effectiveness and the scalability of the approach. Using an inexpensive cloud computing service of Amazon, we draw graphs with ten million edges in about 60 minutes.

Connected through hyperedge HE-1 (induced by source paper "batchlayout a batch parallel force directed graph layout algorithm in shared memory"):
  with members:
    - "multi level tree based approach for interactive graph visualization with semantic zoom"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "graph drawing by stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-2 (induced by source paper "givip a visual profiler for distributed graph processing systems"):
  with members:
    - "a fraud detection visualization system utilizing radial drawings and heat maps"

Connected through hyperedge HE-3 (induced by source paper "drawing large graphs by multilevel maxent stress optimization"):
  with members:
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "a sparse stress model"
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "partitioning complex networks via size constrained clustering"
    - "coast a convex optimization approach to stress based embedding"
    - "engineering a scalable high quality graph partitioner"

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)