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: "tensor prediction rademacher complexity and random 3 xor"
  Abstract: Here we study the tensor prediction problem, where the goal is to accurately predict the entries of a low rank, third-order tensor (with noise) given as few observations as possible. We give algorithms based on the sixth level of the sum-of-squares hierarchy that work with roughly $m = n^{3/2}$observations, and we complement our result by showing that any attempt to solve tensor prediction with $m = n^{3/2 - \epsilon}$ observations through the sum-of-squares hierarchy needs $n^{2 \epsilon}$ rounds and consequently would run in moderately exponential time. In contrast, information theoretically roughly $m = n$ observations suffice. #R##N#This work is part of a broader agenda of studying computational vs. statistical tradeoffs through the sum-of-squares hierarchy. In particular, for linear inverse problems (such as tensor prediction) the natural sum-of-squares relaxation gives rise to a sequence of norms. Our approach is to characterize their Rademacher complexity. Moreover, both our upper and lower bounds are based on connections between this, and the task of strongly refuting random $3$-XOR formulas, and the resolution proof system.

Connected through hyperedge HE-1 (induced by source paper "extended formulation lower bounds for refuting random csps"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (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"
    - "nuclear norm of higher order tensors"
    - "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-3 (induced by source paper "low rank tensor recovery via iterative hard thresholding"):
  with members:
    - "tensor theta norms and low rank recovery"
    - "the restricted isometry property of subsampled fourier matrices"
    - "stable low rank matrix recovery via null space properties"
    - "approximation algorithms for model based compressive sensing"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "structured random measurements in signal processing"
    - "low rank matrix recovery from rank one measurements"
    - "compressive multiplexing of correlated signals"

Connected through hyperedge HE-4 (induced by source paper "strongly refuting random csps below the spectral threshold"):
  with members:
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor principal component analysis via sum of squares proofs"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-5 (induced by source paper "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "a lasserre based 1 varepsilon approximation for pm mid p_j 1 textrm prec mid c_ max"
    - "learning mixtures of gaussians in high dimensions"
    - "sum of squares lower bounds for sparse pca"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor principal component analysis via sum of squares proofs"
    - "powers of tensors and fast matrix multiplication"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-6 (induced by source paper "speeding up sum of squares for tensor decomposition and planted sparse vectors"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "learning mixtures of gaussians in high dimensions"
    - "sum of squares lower bounds for sparse pca"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor principal component analysis via sum of squares proofs"
    - "a statistical model for tensor pca"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "a tensor approach to learning mixed membership community models"

Connected through hyperedge HE-7 (induced by source paper "decomposing overcomplete 3rd order tensors using sum of squares algorithms"):
  with members:
    - "learning mixtures of gaussians in high dimensions"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "smoothed analysis of tensor decompositions"
    - "online tensor methods for learning latent variable models"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"

Connected through hyperedge HE-8 (induced by source paper "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for sparse pca"
    - "sum of squares lower bounds from pairwise independence"
    - "tight lower bounds for planted clique in the degree 4 sos program"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares proofs and the quest toward optimal algorithms"

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)