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: "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
  Abstract: Given a dataset of points in a metric space and an integer $k$, a diversity maximization problem requires determining a subset of $k$ points maximizing some diversity objective measure, e.g., the minimum or the average distance between two points in the subset. Diversity maximization is computationally hard, hence only approximate solutions can be hoped for. Although its applications are mainly in massive data analysis, most of the past research on diversity maximization focused on the sequential setting. In this work we present space and pass/round-efficient diversity maximization algorithms for the Streaming and MapReduce models and analyze their approximation guarantees for the relevant class of metric spaces of bounded doubling dimension. Like other approaches in the literature, our algorithms rely on the determination of high-quality core-sets, i.e., (much) smaller subsets of the input which contain good approximations to the optimal solution for the whole input. For a variety of diversity objective functions, our algorithms attain an $(\alpha+\epsilon)$-approximation ratio, for any constant $\epsilon>0$, where $\alpha$ is the best approximation ratio achieved by a polynomial-time, linear-space sequential algorithm for the same diversity objective. This improves substantially over the approximation ratios attainable in Streaming and MapReduce by state-of-the-art algorithms for general metric spaces. We provide extensive experimental evidence of the effectiveness of our algorithms on both real world and synthetic datasets, scaling up to over a billion points.

Connected through hyperedge HE-1 (induced by source paper "coreset based strategies for robust center type problems"):
  with members:
    - "a general coreset based approach to diversity maximization under matroid constraints"
    - "small space stream summary for matroid center"
    - "generalized center problems with outliers"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "a lottery model for center type problems with outliers"
    - "matroid and knapsack center problems"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "a general coreset based approach to diversity maximization under matroid constraints"):
  with members:
    - "diversity maximization in doubling metrics"
    - "local search for max sum diversification"
    - "efficient classification for metric data"

Connected through hyperedge HE-3 (induced by source paper "on clustering incomplete data"):
  with members:
    - "parameterized k clustering the distance matters"
    - "approximation schemes for low rank binary matrix approximation problems"
    - "parameterized algorithms for the matrix completion problem"
    - "approximation algorithms for stochastic clustering"
    - "parameterized low rank binary matrix approximation"
    - "independence clustering without a matrix"
    - "dispersion on trees"
    - "computational limits for matrix completion"

Connected through hyperedge HE-4 (induced by source paper "composable core sets for determinant maximization a simple near optimal algorithm"):
  with members:
    - "composable core sets for determinant maximization problems via spectral spanners"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "how to be fair and diverse"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "streaming algorithms for submodular function maximization"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-5 (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"
    - "randomized composable core sets for distributed submodular maximization"
    - "approximating k median via pseudo approximation"
    - "fast clustering using mapreduce"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-6 (induced by source paper "improving distributed similarity join in metric space with error bounded sampling"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "adaptive sampling for rapidly matching histograms"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "load balancing for mapreduce based entity resolution"

Connected through hyperedge HE-7 (induced by source paper "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"):
  with members:
    - "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"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-8 (induced by source paper "composable core sets for determinant maximization problems via spectral spanners"):
  with members:
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "real stable polynomials and matroids optimization and counting"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized rounding for the largest simplex problem"
    - "linear bandits in high dimension and recommendation systems"
    - "learning determinantal point processes"
    - "exponential inapproximability of selecting a maximum volume sub matrix"

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)