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: "rounding sum of squares relaxations"
  Abstract: We present a general approach to rounding semidefinite programming relaxations obtained by the Sum-of-Squares method (Lasserre hierarchy). Our approach is based on using the connection between these relaxations and the Sum-of-Squares proof system to transform a *combining algorithm* -- an algorithm that maps a distribution over solutions into a (possibly weaker) solution -- into a *rounding algorithm* that maps a solution of the relaxation to a solution of the original problem. #R##N#Using this approach, we obtain algorithms that yield improved results for natural variants of three well-known problems: #R##N#1) We give a quasipolynomial-time algorithm that approximates the maximum of a low degree multivariate polynomial with non-negative coefficients over the Euclidean unit sphere. Beyond being of interest in its own right, this is related to an open question in quantum information theory, and our techniques have already led to improved results in this area (Brand\~{a}o and Harrow, STOC '13). #R##N#2) We give a polynomial-time algorithm that, given a d dimensional subspace of R^n that (almost) contains the characteristic function of a set of size n/k, finds a vector $v$ in the subspace satisfying $|v|_4^4 > c(k/d^{1/3}) |v|_2^2$, where $|v|_p = (E_i v_i^p)^{1/p}$. Aside from being a natural relaxation, this is also motivated by a connection to the Small Set Expansion problem shown by Barak et al. (STOC 2012) and our results yield a certain improvement for that problem. #R##N#3) We use this notion of L_4 vs. L_2 sparsity to obtain a polynomial-time algorithm with substantially improved guarantees for recovering a planted $\mu$-sparse vector v in a random d-dimensional subspace of R^n. If v has mu n nonzero coordinates, we can recover it with high probability whenever $\mu < O(\min(1,n/d^2))$, improving for $d < n^{2/3}$ prior methods which intrinsically required $\mu < O(1/\sqrt(d))$.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics"):
  with members:
    - "bayesian estimation from few samples community detection and related problems"
    - "provable learning of noisy or networks"
    - "tensor principal component analysis via sum of squares proofs"
    - "hypercontractivity sum of squares proofs and their applications"
    - "from statistical knowledge bases to degrees of belief"
    - "characterizing and reasoning about probabilistic and non probabilistic expectation"

Connected through hyperedge HE-3 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-4 (induced by source paper "the power of sum of squares for detecting hidden structures"):
  with members:
    - "exact tensor completion with sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "how well do local algorithms solve semidefinite programs"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"

Connected through hyperedge HE-5 (induced by source paper "sum of squares lower bounds from symmetry and a good story"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on the sum of squares degree of symmetric quadratic functions"
    - "sum of squares lower bounds from pairwise independence"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-6 (induced by source paper "finding a sparse vector in a subspace linear sparsity using alternating directions"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "simple efficient and neural algorithms for sparse coding"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "sum of squares lower bounds for sparse pca"
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"
    - "finding a low rank basis in a matrix subspace"
    - "dual principal component pursuit"

Connected through hyperedge HE-7 (induced by source paper "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"):
  with members:
    - "sum of square proof for brascamp lieb type inequality"
    - "information theoretic perspectives on brascamp lieb inequality and its reverse"
    - "polynomial time tensor decompositions with sum of squares"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "operator scaling theory and applications"
    - "constructive noncommutative rank computation in deterministic polynomial time over fields of arbitrary characteristics"
    - "constructive noncommutative rank computation is in deterministic polynomial time"
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"

Connected through hyperedge HE-8 (induced by source paper "weak decoupling polynomial folds and approximate optimization over the sphere"):
  with members:
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "sum of squares certificates for maxima of random tensors on the sphere"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "strongly refuting random csps below the spectral threshold"
    - "decomposing overcomplete 3rd order tensors using sum of squares 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.DS(Data Structures and Algorithms)