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: "fast clustering using mapreduce"
  Abstract: Clustering problems have numerous applications and are becoming more challenging as the size of the data increases. In this paper, we consider designing clustering algorithms that can be used in MapReduce, the most popular programming environment for processing large datasets. We focus on the practical and popular clustering problems, $k$-center and $k$-median. We develop fast clustering algorithms with constant factor approximation guarantees. From a theoretical perspective, we give the first analysis that shows several clustering algorithms are in $\mathcal{MRC}^0$, a theoretical MapReduce class introduced by Karloff et al. \cite{KarloffSV10}. Our algorithms use sampling to decrease the data size and they run a time consuming clustering algorithm such as local search or Lloyd's algorithm on the resulting data set. Our algorithms have sufficient flexibility to be used in practice since they run in a constant number of MapReduce rounds. We complement these results by performing experiments using our algorithms. We compare the empirical performance of our algorithms to several sequential and parallel algorithms for the $k$-median problem. The experiments show that our algorithms' solutions are similar to or better than the other algorithms' solutions. Furthermore, on data sets that are sufficiently large, our algorithms are faster than the other parallel algorithms that we tested.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-3 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"):
  with members:
    - "simple concurrent labeling algorithms for connected components"
    - "simple graph coloring algorithms for congested clique and massively parallel computation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "parallel graph connectivity in log diameter rounds"
    - "brief announcement semi mapreduce meets congested clique"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-4 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "in database learning with sparse tensors"

Connected through hyperedge HE-5 (induced by source paper "a conditional lower bound on graph connectivity in mapreduce"):
  with members:
    - "on the complexity of list ranking in the parallel external memory model"
    - "on the computational complexity of mapreduce"
    - "parallel algorithms for geometric graph problems"
    - "communication steps for parallel query processing"
    - "densest subgraph in streaming and mapreduce"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "scalable k means"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-6 (induced by source paper "a composable coreset for k center in doubling metrics"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "efficient parallel algorithms for k center clustering"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "randomized composable core sets for distributed submodular maximization"
    - "approximating k median via pseudo approximation"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-7 (induced by source paper "accurate mapreduce algorithms for k median and k means in general metric spaces"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "coresets and sketches"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "space round tradeoffs for mapreduce computations"
    - "a unified framework for approximating and clustering data"
    - "simpler analyses of local search algorithms for facility location"

Connected through hyperedge HE-8 (induced by source paper "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"):
  with members:
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "clustering uncertain graphs"
    - "a practical parallel algorithm for diameter approximation of massive weighted graphs"
    - "randomized composable core sets for distributed submodular maximization"
    - "space and time efficient parallel graph decomposition clustering and diameter approximation"
    - "distributed representations of words and phrases and their compositionality"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

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)