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: "approximate kernel clustering"
  Abstract: In the kernel clustering problem we are given a large $n\times n$ positive semi-definite matrix $A=(a_{ij})$ with $\sum_{i,j=1}^na_{ij}=0$ and a small $k\times k$ positive semi-definite matrix $B=(b_{ij})$. The goal is to find a partition $S_1,...,S_k$ of $\{1,... n\}$ which maximizes the quantity $$ \sum_{i,j=1}^k (\sum_{(i,j)\in S_i\times S_j}a_{ij})b_{ij}. $$ We study the computational complexity of this generic clustering problem which originates in the theory of machine learning. We design a constant factor polynomial time approximation algorithm for this problem, answering a question posed by Song, Smola, Gretton and Borgwardt. In some cases we manage to compute the sharp approximation threshold for this problem assuming the Unique Games Conjecture (UGC). In particular, when $B$ is the $3\times 3$ identity matrix the UGC hardness threshold of this problem is exactly $\frac{16\pi}{27}$. We present and study a geometric conjecture of independent interest which we show would imply that the UGC threshold when $B$ is the $k\times k$ identity matrix is $\frac{8\pi}{9}(1-\frac{1}{k})$ for every $k\ge 3$.

Connected through hyperedge HE-1 (induced by source paper "how bad is worst case data if you know where it comes from"):
  with members:
    - "regression from dependent observations"
    - "list decodable linear regression"
    - "a theory of selective prediction"
    - "efficient algorithms for outlier robust regression"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"

Connected through hyperedge HE-2 (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"
    - "grothendieck type inequalities in combinatorial optimization"

Connected through hyperedge HE-3 (induced by source paper "a sub constant improvement in approximating the positive semidefinite grothendieck problem"):
  with members:
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

Connected through hyperedge HE-4 (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"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

Connected through hyperedge HE-5 (induced by source paper "grothendieck type inequalities in combinatorial optimization"):
  with members:
    - "the p versus np problem"
    - "approximating matrix p norms"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "solution of the propeller conjecture in mathbb r 3"):
  with members:
    - "grothendieck type inequalities in combinatorial optimization"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

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)