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: "grothendieck type inequalities in combinatorial optimization"
  Abstract: We survey connections of the Grothendieck inequality and its variants to combinatorial optimization and computational complexity.

Connected through hyperedge HE-1 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-2 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "towards practical differential privacy for sql queries"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "an improved private mechanism for small databases"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-3 (induced by source paper "a new algorithm for the robust semi random independent set problem"):
  with members:
    - "does robustness imply tractability a lower bound for planted clique in the semi random model"
    - "learning from untrusted data"
    - "constant factor approximation for balanced cut in the pie model"
    - "how to play unique games against a semi random adversary"

Connected through hyperedge HE-4 (induced by source paper "grothendieck constant is norm of strassen matrix multiplication tensor"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "nuclear norm of higher order tensors"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

Connected through hyperedge HE-5 (induced by source paper "computing the partition function of a polynomial on the boolean cube"):
  with members:
    - "beating the random assignment on constraint satisfaction problems of bounded degree"

Connected through hyperedge HE-6 (induced by source paper "semidefinite programs on sparse random graphs and their application to community detection"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "how robust are reconstruction thresholds for community detection"
    - "exact recovery in the stochastic block model"
    - "multireference alignment using semidefinite programming"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-7 (induced by source paper "semidefinite programs on sparse random graphs"):
  with members:
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-8 (induced by source paper "tight hardness of the non commutative grothendieck problem"):
  with members:
    - "analysis of boolean functions"
    - "efficient rounding for the noncommutative grothendieck inequality"
    - "approximate kernel clustering"

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)