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: "complexity measures for map reduce and comparison to parallel computing"
  Abstract: The programming paradigm Map-Reduce and its main open-source implementation, Hadoop, have had an enormous impact on large scale data processing. Our goal in this expository writeup is two-fold: first, we want to present some complexity measures that allow us to talk about Map-Reduce algorithms formally, and second, we want to point out why this model is actually different from other models of parallel programming, most notably the PRAM (Parallel Random Access Memory) model. We are looking for complexity measures that are detailed enough to make fine-grained distinction between different algorithms, but which also abstract away many of the implementation details.

Connected through hyperedge HE-1 (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-2 (induced by source paper "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"):
  with members:
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "powers of tensors and fast matrix multiplication"
    - "parallel algorithms for geometric graph problems"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "exact weight subgraphs and the k sum conjecture"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-3 (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"
    - "scalable k means"
    - "sorting searching and simulation in the mapreduce framework"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-4 (induced by source paper "massively parallel dynamic programming on trees"):
  with members:
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "communication steps for parallel query processing"
    - "densest subgraph in streaming and mapreduce"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-5 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "the unified logging infrastructure for data analytics at twitter"
    - "dimension independent similarity computation"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-6 (induced by source paper "security and privacy aspects in mapreduce on clouds a survey"):
  with members:
    - "assignment problems of different sized inputs in mapreduce"
    - "computing marginals using mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "enumerating subgraph instances using map reduce"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "efficient multi way theta join processing using mapreduce"

Connected through hyperedge HE-7 (induced by source paper "dimension independent matrix square using mapreduce"):
  with members:
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "low rank approximation and regression in input sparsity time"
    - "dimension independent similarity computation"
    - "fast approximation of matrix coherence and statistical leverage"
    - "faster least squares approximation"

Connected through hyperedge HE-8 (induced by source paper "dimension independent similarity computation"):
  with members:
    - "dimension independent matrix square using mapreduce"
    - "finding associations and computing similarity via biased pair sampling"

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)