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: "approximating the little grothendieck problem over the orthogonal and unitary groups"
  Abstract: The little Grothendieck problem consists of maximizing $\sum_{ij}C_{ij}x_ix_j$ over binary variables $x_i\in\{\pm1\}$, where C is a positive semidefinite matrix. In this paper we focus on a natural generalization of this problem, the little Grothendieck problem over the orthogonal group. Given C a dn x dn positive semidefinite matrix, the objective is to maximize $\sum_{ij}Tr (C_{ij}^TO_iO_j^T)$ restricting $O_i$ to take values in the group of orthogonal matrices, where $C_{ij}$ denotes the (ij)-th d x d block of C. We propose an approximation algorithm, which we refer to as Orthogonal-Cut, to solve this problem and show a constant approximation ratio. Our method is based on semidefinite programming. For a given $d\geq 1$, we show a constant approximation ratio of $\alpha_{R}(d)^2$, where $\alpha_{R}(d)$ is the expected average singular value of a d x d matrix with random Gaussian $N(0,1/d)$ i.i.d. entries. For d=1 we recover the known $\alpha_{R}(1)^2=2/\pi$ approximation guarantee for the classical little Grothendieck problem. Our algorithm and analysis naturally extends to the complex valued case also providing a constant approximation ratio for the analogous problem over the Unitary Group. #R##N#Orthogonal-Cut also serves as an approximation algorithm for several applications, including the Procrustes problem where it improves over the best previously known approximation ratio of~$\frac1{2\sqrt{2}}$. The little Grothendieck problem falls under the class of problems approximated by a recent algorithm proposed in the context of the non-commutative Grothendieck inequality. Nonetheless, our approach is simpler and it provides a more efficient algorithm with better approximation ratios and matching integrality gaps. #R##N#Finally, we also provide an improved approximation algorithm for the more general little Grothendieck problem over the orthogonal (or unitary) group with rank constraints.

Connected through hyperedge HE-1 (induced by source paper "multi frequency phase synchronization"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "information recovery from pairwise measurements"
    - "global registration of multiple point clouds using semidefinite programming"

Connected through hyperedge HE-2 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-3 (induced by source paper "non unique games over compact groups and orientation estimation in cryo em"):
  with members:
    - "synchronization over z2 and community detection in signed multiplex networks with constraints"
    - "near optimal joint object matching via convex relaxation"
    - "fast steerable principal component analysis"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"
    - "multireference alignment using semidefinite programming"
    - "efficient rounding for the noncommutative grothendieck inequality"

Connected through hyperedge HE-4 (induced by source paper "sync rank robust ranking constrained ranking and rank aggregation via eigenvector and semidefinite programming synchronization"):
  with members:
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "active ranking using pairwise comparisons"
    - "the labor economics of paid crowdsourcing"
    - "least squares ranking on graphs"
    - "noisy sorting without resampling"

Connected through hyperedge HE-5 (induced by source paper "decoding binary node labels from censored edge measurements phase transition and efficient recovery"):
  with members:
    - "cramer rao bounds for synchronization of rotations"
    - "near optimal joint object matching via convex relaxation"
    - "exact recovery in the stochastic block model"
    - "exact and stable recovery of rotations for robust synchronization"
    - "community detection in the labelled stochastic block model"
    - "phase retrieval with polarization"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-6 (induced by source paper "global registration of multiple point clouds using semidefinite programming"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "incremental computation of pseudo inverse of laplacian theory and applications"
    - "exact and stable recovery of rotations for robust synchronization"
    - "efficient rounding for the noncommutative grothendieck inequality"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "estimation and registration on graphs"

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)