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: "learning from multiway data simple and efficient tensor regression"
  Abstract: Tensor regression has shown to be advantageous in learning tasks with multi-directional relatedness. Given massive multiway data, traditional methods are often too slow to operate on or suffer from memory bottleneck. In this paper, we introduce subsampled tensor projected gradient to solve the problem. Our algorithm is impressively simple and efficient. It is built upon projected gradient method with fast tensor power iterations, leveraging randomized sketching for further acceleration. Theoretical analysis shows that our algorithm converges to the correct solution in fixed number of iterations. The memory requirement grows linearly with the size of the problem. We demonstrate superior empirical performance on both multi-linear multi-task learning and spatio-temporal applications.

Connected through hyperedge HE-1 (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-2 (induced by source paper "tensor regression using low rank and sparse tucker decompositions"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "low rank tensor recovery via iterative hard thresholding"
    - "lower bounds for sparse recovery"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "guaranteed rank minimization via singular value projection"
    - "most tensor problems are np hard"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "sparse and low rank tensor regression via parallel proximal method"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "sturm sparse tubal regularized multilinear regression for fmri"
    - "tensor regression meets gaussian processes"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "fast and scalable estimator for sparse and unit rank higher order regression models"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "sturm sparse tubal regularized multilinear regression for fmri"
    - "tensor regression meets gaussian processes"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-5 (induced by source paper "long term forecasting using higher order tensor rnns"):
  with members:
    - "learning to reason with third order tensor products"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "generating long term trajectories using deep hierarchical networks"
    - "higher order recurrent neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "polynomial time tensor decompositions with sum of squares"
    - "reinforcement learning of pomdps using spectral methods"
    - "tensorly tensor learning in python"
    - "tensor factorization via matrix factorization"
    - "learning mixtures of gaussians in high dimensions"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "tensor decompositions for modeling inverse dynamics"):
  with members:
    - "adam a method for stochastic optimization"
    - "tensor networks for big data analytics and large scale optimization problems"

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.LG(Machine Learning)