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: "design generation and validation of extreme scale power law graphs"
  Abstract: Massive power-law graphs drive many fields: metagenomics, brain mapping, Internet-of-things, cybersecurity, and sparse machine learning. The development of novel algorithms and systems to process these data requires the design, generation, and validation of enormous graphs with exactly known properties. Such graphs accelerate the proper testing of new algorithms and systems and are a prerequisite for success on real applications. Many random graph generators currently exist that require realizing a graph in order to know its exact properties: number of vertices, number of edges, degree distribution, and number of triangles. Designing graphs using these random graph generators is a time-consuming trial-and-error process. This paper presents a novel approach that uses Kronecker products to allow the exact computation of graph properties prior to graph generation. In addition, when a real graph is desired, it can be generated quickly in memory on a parallel computer with no-interprocessor communication. To test this approach, graphs with 1012 edges are generated on a 40,000+ core supercomputer in 1 second and exactly agree with those predicted by the theory. In addition, to demonstrate the extensibility of this approach, decetta-scale graphs with up to 10^30 edges are simulated in a few minutes on a laptop.

Connected through hyperedge HE-1 (induced by source paper "a fast and efficient incremental approach toward dynamic community detection"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "streaming graph challenge stochastic block partition"
    - "parallel heuristics for scalable community detection"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-2 (induced by source paper "optimizing xeon phi for interactive data analysis"):
  with members:
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "tabularosa tabular operating system architecture for massively parallel heterogeneous compute engines"
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "interactive launch of 16 000 microsoft windows instances on a supercomputer"

Connected through hyperedge HE-3 (induced by source paper "on large scale graph generation with validation of diverse triangle statistics at edges and vertices"):
  with members:
    - "distributed triangle counting in the graphulo matrix math library"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "directed closure measures for networks with reciprocity"

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)