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: "nuclear norm of higher order tensors"
  Abstract: We establish several mathematical and computational properties of the nuclear norm for higher-order tensors. We show that like tensor rank, tensor nuclear norm is dependent on the choice of base field --- the value of the nuclear norm of a real 3-tensor depends on whether we regard it as a real 3-tensor or a complex 3-tensor with real entries. We show that every tensor has a nuclear norm attaining decomposition and every symmetric tensor has a symmetric nuclear norm attaining decomposition. There is a corresponding notion of nuclear rank that, unlike tensor rank, is upper semicontinuous. We establish an analogue of Banach's theorem for tensor spectral norm and Comon's conjecture for tensor rank --- for a symmetric tensor, its symmetric nuclear norm always equals its nuclear norm. We show that computing tensor nuclear norm is NP-hard in several sense. Deciding weak membership in the nuclear norm unit ball of 3-tensors is NP-hard, as is finding an $\varepsilon$-approximation of nuclear norm for 3-tensors. In addition, the problem of computing spectral or nuclear norm of a 4-tensor is NP-hard, even if we restrict the 4-tensor to be bi-Hermitian, bisymmetric, positive semidefinite, nonnegative valued, or all of the above. We discuss some simple polynomial-time approximation bounds. As an aside, we show that the nuclear $(p,q)$-norm of a matrix is NP-hard in general but can be computed in polynomial-time if $p=1$, $q = 1$, or $p=q=2$, with closed-form expressions for the nuclear $(1,q)$- and $(p,1)$-norms.

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"
    - "parallel tensor compression for large scale scientific data"

Connected through hyperedge HE-2 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "exact tensor completion with sum of squares"
    - "sum of squares lower bounds for refuting any csp"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning without concentration"
    - "a new convex relaxation for tensor completion"

Connected through hyperedge HE-3 (induced by source paper "iterative collaborative filtering for sparse noisy tensor estimation"):
  with members:
    - "exact tensor completion with sum of squares"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "spectral algorithms for tensor completion"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "understanding alternating minimization for matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-4 (induced by source paper "tensor p shrinkage nuclear norm for low rank tensor completion"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "smooth parafac decomposition for tensor completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-5 (induced by source paper "tensor q rank a new data dependent tensor rank"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "parallel matrix factorization for low rank tensor completion"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-6 (induced by source paper "tensor recovery from noisy and multi level quantized measurements"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "distributed flexible nonlinear tensor factorization"
    - "parallel matrix factorization for low rank tensor completion"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "a statistical model for tensor pca"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-7 (induced by source paper "near optimal sample complexity for convex tensor completion"):
  with members:
    - "matrix completion via max norm constrained optimization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "exact tensor completion using t svd"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "concentration based guarantees for low rank matrix reconstruction"

Connected through hyperedge HE-8 (induced by source paper "grothendieck constant is norm of strassen matrix multiplication tensor"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "grothendieck type inequalities in combinatorial optimization"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

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.CC(Computational Complexity)