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: "frogwild fast pagerank approximations on graph engines"
  Abstract: We propose FrogWild, a novel algorithm for fast approximation of high PageRank vertices, geared towards reducing network costs of running traditional PageRank algorithms. Our algorithm can be seen as a quantized version of power iteration that performs multiple parallel random walks over a directed graph. One important innovation is that we introduce a modification to the GraphLab framework that only partially synchronizes mirror vertices. This partial synchronization vastly reduces the network traffic generated by traditional PageRank algorithms, thus greatly reducing the per-iteration cost of PageRank. On the other hand, this partial synchronization also creates dependencies between the random walks used to estimate PageRank. Our main theoretical innovation is the analysis of the correlations introduced by this partial synchronization process and a bound establishing that our approximation is close to the true PageRank vector. #R##N#We implement our algorithm in GraphLab and compare it against the default PageRank implementation. We show that our algorithm is very fast, performing each iteration in less than one second on the Twitter graph and can be up to 7x faster compared to the standard GraphLab PageRank implementation.

Connected through hyperedge HE-1 (induced by source paper "hogwild gibbs can be panaccurate"):
  with members:
    - "testing ising models"
    - "dimmwitted a study of main memory statistical analytics"

Connected through hyperedge HE-2 (induced by source paper "a survey of state management in big data processing systems"):
  with members:
    - "elasticutor rapid elasticity for realtime stateful stream processing"
    - "incremental query processing on big data streams"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "incremental computation with names"
    - "s store streaming meets transaction processing"
    - "tolerating correlated failures in massively parallel stream processing engines"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-3 (induced by source paper "approximate personalized pagerank on dynamic graphs"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "efficient algorithms for personalized pagerank"
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "on the complexity of the monte carlo method for incremental pagerank"
    - "pagerank beyond the web"
    - "defining and evaluating network communities based on ground truth"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-4 (induced by source paper "ensuring rapid mixing and low bias for asynchronous gibbs sampling"):
  with members:
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "incremental knowledge base construction using deepdive"
    - "taming the wild a unified analysis of hogwild style algorithms"
    - "dimmwitted a study of main memory statistical 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)