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: "space round tradeoffs for mapreduce computations"
  Abstract: This work explores fundamental modeling and algorithmic issues arising in the well-established MapReduce framework. First, we formally specify a computational model for MapReduce which captures the functional flavor of the paradigm by allowing for a flexible use of parallelism. Indeed, the model diverges from a traditional processor-centric view by featuring parameters which embody only global and local memory constraints, thus favoring a more data-centric view. Second, we apply the model to the fundamental computation task of matrix multiplication presenting upper and lower bounds for both dense and sparse matrix multiplication, which highlight interesting tradeoffs between space and round complexity. Finally, building on the matrix multiplication results, we derive further space-round tradeoffs on matrix inversion and matching.

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 "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"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "matroid and knapsack center problems"

Connected through hyperedge HE-3 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation"):
  with members:
    - "accurate mapreduce algorithms for k median and k means in general metric spaces"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "clustering uncertain graphs"
    - "k means for streaming and distributed big sparse data"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-4 (induced by source paper "a survey on geographically distributed big data processing using mapreduce"):
  with members:
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "towards geo distributed machine learning"
    - "the bigdawg polystore system and architecture"
    - "tensorflow a system for large scale machine learning"
    - "medusa an efficient cloud fault tolerant mapreduce"
    - "mllib machine learning in apache spark"
    - "assignment problems of different sized inputs in mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"

Connected through hyperedge HE-5 (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"
    - "fast clustering using mapreduce"
    - "a unified framework for approximating and clustering data"
    - "simpler analyses of local search algorithms for facility location"

Connected through hyperedge HE-6 (induced by source paper "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "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"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"

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:
    - "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"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-8 (induced by source paper "adaptive mapreduce similarity joins"):
  with members:
    - "locality sensitive hashing of curves"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "experimental evaluation of multi round matrix multiplication on mapreduce"
    - "i o efficient similarity join"
    - "communication steps for parallel query processing"

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.DS(Data Structures and Algorithms)