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 practical parallel algorithm for diameter approximation of massive weighted graphs"
  Abstract: We present a space and time efficient practical parallel algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. The core of the algorithm is a weighted graph decomposition strategy generating disjoint clusters of bounded weighted radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; moreover, for important practical classes of graphs, it runs in a number of rounds asymptotically smaller than those required by the natural approximation provided by the state-of-the-art $\Delta$-stepping SSSP algorithm, which is its only practical linear-space competitor in the aforementioned computational scenario. We complement our theoretical findings with an extensive experimental analysis on large benchmark graphs, which demonstrates that our algorithm attains substantial improvements on a number of key performance indicators with respect to the aforementioned competitor, while featuring a similar approximation ratio (a small constant less than 1.4, as opposed to the polylogarithmic theoretical bound).

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "max sum diversity via convex programming"
    - "space and time efficient parallel graph decomposition clustering and diameter approximation"
    - "efficient classification for metric data"
    - "space round tradeoffs for mapreduce computations"
    - "maximum dispersion and geometric maximum weight cliques"

Connected through hyperedge HE-3 (induced by source paper "efficient parallel algorithms for k center clustering"):
  with members:
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "scalable k means"
    - "fast clustering using mapreduce"
    - "parallel approximation algorithms for facility location problems"

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)