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: "era of big data processing a new approach via tensor networks and tensor decompositions"
  Abstract: Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such massive multidimensional data via suitable low-rank approximations. Big data analytics require novel technologies to efficiently process huge datasets within tolerable elapsed times. Such a new emerging technology for multidimensional big data is a multiway analysis via tensor networks (TNs) and tensor decompositions (TDs) which represent tensors by sets of factor (component) matrices and lower-order (core) tensors. Dynamic tensor analysis allows us to discover meaningful hidden structures of complex data and to perform generalizations by capturing multi-linear and multi-aspect relationships. We will discuss some fundamental TN models, their mathematical and graphical descriptions and associated learning algorithms for large-scale TDs and TNs, with many potential applications including: Anomaly detection, feature extraction, classification, cluster analysis, data fusion and integration, pattern recognition, predictive modeling, regression, time series analysis and multiway component analysis. #R##N#Keywords: Large-scale HOSVD, Tensor decompositions, CPD, Tucker models, Hierarchical Tucker (HT) decomposition, low-rank tensor approximations (LRA), Tensorization/Quantization, tensor train (TT/QTT) - Matrix Product States (MPS), Matrix Product Operator (MPO), DMRG, Strong Kronecker Product (SKP).

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"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "simtensor a synthetic tensor data generator"
    - "high performance tensor contraction without transposition"
    - "tensorizing neural networks"
    - "the gap benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "dynamic spatiotemporal graph neural network with tensor network"):
  with members:
    - "graph wavenet for deep spatial temporal graph modeling"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "coupled tensor completion via low rank tensor ring"):
  with members:
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "efficient contraction of large tensor networks for weighted model counting through graph decompositions"):
  with members:
    - "fast counting with tensor networks"
    - "benchmarking treewidth as a practical component of tensor network simulations"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "answer set solving with bounded treewidth revisited"
    - "positive instance driven dynamic programming for treewidth"
    - "faq questions asked frequently"
    - "graph bisection with pareto optimization"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-6 (induced by source paper "tensor grid decomposition with application to tensor completion"):
  with members:
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "exact recovery of low rank tensor decomposition under reshuffling"
    - "learning efficient tensor representations with ring structure networks"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "randomized tensor ring decomposition and its application to large scale data reconstruction"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "randomized cp tensor decomposition"
    - "tensor ring decomposition"
    - "decomposition of big tensors with low multilinear rank"

Connected through hyperedge HE-8 (induced by source paper "discovering hidden structure in high dimensional human behavioral data via tensor factorization"):
  with members:
    - "non negative tensor factorization for human behavioral pattern mining in online games"

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.ET(Emerging Technologies)