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: "parallel tensor compression for large scale scientific data"
  Abstract: As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data, assuming double precision. By viewing the data as a dense five-way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 5000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed-memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.

Connected through hyperedge HE-1 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "multiscale analysis for higher order tensors"
    - "generating long term trajectories using deep hierarchical networks"
    - "tensor regression meets gaussian processes"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "tensorly tensor learning in python"
    - "why should i trust you explaining the predictions of any classifier"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-3 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"
    - "improved bounds on sample size for implicit matrix trace estimators"

Connected through hyperedge HE-4 (induced by source paper "low rank tucker approximation of a tensor from streaming data"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "tthresh tensor compression for multidimensional visual data"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "practical sketching algorithms for low rank matrix approximation"
    - "decomposition of big tensors with low multilinear rank"
    - "tensor decompositions a new concept in brain data analysis"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-5 (induced by source paper "l1 norm tucker tensor decomposition"):
  with members:
    - "l1 norm principal component analysis of complex data"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "the exact solution to rank 1 l1 norm tucker2 decomposition"
    - "efficient l1 norm principal component analysis via bit flipping"
    - "classification via tensor decompositions of echo state networks"
    - "optimal algorithms for l_ 1 subspace signal processing"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-6 (induced by source paper "unsupervised machine learning based on non negative tensor factorization for analyzing reactive mixing"):
  with members:
    - "identification of release sources in advection diffusion system by machine learning combined with green s function inverse method"
    - "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies"
    - "nonnegative matrix factorization for identification of unknown number of sources emitting delayed signals"
    - "on mesh restrictions to satisfy comparison principles maximum principles and the non negative constraint recent developments and new results"
    - "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-7 (induced by source paper "tuckermpi a parallel c mpi software package for large scale data compression via the tucker tensor decomposition"):
  with members:
    - "tthresh tensor compression for multidimensional visual data"
    - "parallel nonnegative cp decomposition of dense tensors"
    - "scalable tucker factorization for sparse tensors algorithms and discoveries"
    - "on optimizing distributed tucker decomposition for dense tensors"
    - "shared memory parallelization of mttkrp for dense tensors"

Connected through hyperedge HE-8 (induced by source paper "z checker a framework for assessing lossy compression of scientific data"):
  with members:
    - "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"
    - "universal numerical encoder and profiler reduces computing s memory wall with software fpga and soc implementations"
    - "the earth system grid supporting the next generation of climate modeling research"

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)