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 unified optimization approach for sparse tensor operations on gpus"
  Abstract: Sparse tensors appear in many large-scale applications with multidimensional and sparse data. While multidimensional sparse data often need to be processed on manycore processors, attempts to develop highly-optimized GPU-based implementations of sparse tensor operations are rare. The irregular computation patterns and sparsity structures as well as the large memory footprints of sparse tensor operations make such implementations challenging. We leverage the fact that sparse tensor operations share similar computation patterns to propose a unified tensor representation called F-COO. Combined with GPU-specific optimizations, F-COO provides highly-optimized implementations of sparse tensor computations on GPUs. The performance of the proposed unified approach is demonstrated for tensor-based kernels such as the Sparse Matricized Tensor-Times-Khatri-Rao Product (SpMTTKRP) and the Sparse Tensor-Times-Matrix Multiply (SpTTM) and is used in tensor decomposition algorithms. Compared to state-of-the-art work we improve the performance of SpTTM and SpMTTKRP up to 3.7 and 30.6 times respectively on NVIDIA Titan-X GPUs. We implement a CANDECOMP/PARAFAC (CP) decomposition and achieve up to 14.9 times speedup using the unified method over state-of-the-art libraries on NVIDIA Titan-X GPUs.

Connected through hyperedge HE-1 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "spartan scalable parafac2 for large sparse data"
    - "simtensor a synthetic tensor data generator"
    - "high performance tensor contraction without transposition"
    - "tensorizing neural networks"
    - "the gap benchmark suite"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-2 (induced by source paper "automatic generation of efficient sparse tensor format conversion routines"):
  with members:
    - "format abstraction for sparse tensor algebracompilers"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-3 (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"
    - "learning from multiway data simple and efficient tensor regression"

Connected through hyperedge HE-4 (induced by source paper "load balanced sparse mttkrp on gpus"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "format abstraction for sparse tensor algebracompilers"):
  with members:
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "tensorflow a system for large scale machine learning"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "an empirical evaluation of allgatherv on multi gpu systems"):
  with members:
    - "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-7 (induced by source paper "acceleration of tensor product operations for high order finite element methods"):
  with members:
    - "gpu accelerated spectral finite elements on all hex meshes"
    - "tensor contractions with extended blas kernels on cpu and gpu"

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.MS(Mathematical Software)