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: "sharp kernel clustering algorithms and their associated grothendieck inequalities"
  Abstract: In the kernel clustering problem we are given a (large) $n\times n$ symmetric positive semidefinite matrix $A=(a_{ij})$ with $\sum_{i=1}^n\sum_{j=1}^n a_{ij}=0$ and a (small) $k\times k$ symmetric positive semidefinite matrix $B=(b_{ij})$. The goal is to find a partition $\{S_1,...,S_k\}$ of $\{1,... n\}$ which maximizes $ \sum_{i=1}^k\sum_{j=1}^k (\sum_{(p,q)\in S_i\times S_j}a_{pq})b_{ij}$. #R##N#We design a polynomial time approximation algorithm that achieves an approximation ratio of $\frac{R(B)^2}{C(B)}$, where $R(B)$ and $C(B)$ are geometric parameters that depend only on the matrix $B$, defined as follows: if $b_{ij} =  $ is the Gram matrix representation of $B$ for some $v_1,...,v_k\in \R^k$ then $R(B)$ is the minimum radius of a Euclidean ball containing the points $\{v_1, ..., v_k\}$. The parameter $C(B)$ is defined as the maximum over all measurable partitions $\{A_1,...,A_k\}$ of $\R^{k-1}$ of the quantity $\sum_{i=1}^k\sum_{j=1}^k b_{ij} $, where for $i\in \{1,...,k\}$ the vector $z_i\in \R^{k-1}$ is the Gaussian moment of $A_i$, i.e., $z_i=\frac{1}{(2\pi)^{(k-1)/2}}\int_{A_i}xe^{-\|x\|_2^2/2}dx$. We also show that for every $\eps > 0$, achieving an approximation guarantee of $(1-\e)\frac{R(B)^2}{C(B)}$ is Unique Games hard.

Connected through hyperedge HE-1 (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"
    - "grothendieck type inequalities in combinatorial optimization"

Connected through hyperedge HE-2 (induced by source paper "a sub constant improvement in approximating the positive semidefinite grothendieck problem"):
  with members:
    - "approximate kernel clustering"

Connected through hyperedge HE-3 (induced by source paper "euclidean partitions optimizing noise stability"):
  with members:
    - "grothendieck type inequalities in combinatorial optimization"
    - "solution of the propeller conjecture in mathbb r 3"
    - "approximate kernel clustering"

Connected through hyperedge HE-4 (induced by source paper "grothendieck type inequalities in combinatorial optimization"):
  with members:
    - "the p versus np problem"
    - "approximating matrix p norms"
    - "approximate kernel clustering"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-5 (induced by source paper "solution of the propeller conjecture in mathbb r 3"):
  with members:
    - "grothendieck type inequalities in combinatorial optimization"
    - "approximate kernel clustering"

Connected through hyperedge HE-6 (induced by source paper "optimal constant time approximation algorithms and unconditional inapproximability results for every bounded degree csp"):
  with members:
    - "lower bounds on query complexity for testing bounded degree csps"
    - "on the optimality of a class of lp based algorithms"
    - "approximation resistant predicates from pairwise independence"
    - "approximate graph coloring by semidefinite programming"

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)