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: "approximation schemes for low rank binary matrix approximation problems"
  Abstract: We provide a randomized linear time approximation scheme for a generic problem about clustering of binary vectors subject to additional constrains. The new constrained clustering problem encompasses a number of problems and by solving it, we obtain the first linear time-approximation schemes for a number of well-studied fundamental problems concerning clustering of binary vectors and low-rank approximation of binary matrices. Among the problems solvable by our approach are \textsc{Low GF(2)-Rank Approximation}, \textsc{Low Boolean-Rank Approximation}, and various versions of \textsc{Binary Clustering}. For example, for \textsc{Low GF(2)-Rank Approximation} problem, where for an $m\times n$ binary matrix $A$ and integer $r>0$, we seek for a binary matrix $B$ of $GF_2$ rank at most $r$ such that $\ell_0$ norm of matrix $A-B$ is minimum, our algorithm, for any $\epsilon>0$ in time $ f(r,\epsilon)\cdot n\cdot m$, where $f$ is some computable function, outputs a $(1+\epsilon)$-approximate solution with probability at least $(1-\frac{1}{e})$. Our approximation algorithms substantially improve the running times and approximation factors of previous works. We also give (deterministic) PTASes for these problems running in time $n^{f(r)\frac{1}{\epsilon^2}\log \frac{1}{\epsilon}}$, where $f$ is some function depending on the problem. Our algorithm for the constrained clustering problem is based on a novel sampling lemma, which is interesting in its own.

Connected through hyperedge HE-1 (induced by source paper "on clustering incomplete data"):
  with members:
    - "parameterized k clustering the distance matters"
    - "parameterized algorithms for the matrix completion problem"
    - "approximation algorithms for stochastic clustering"
    - "parameterized low rank binary matrix approximation"
    - "independence clustering without a matrix"
    - "dispersion on trees"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "computational limits for matrix completion"

Connected through hyperedge HE-2 (induced by source paper "low rank binary matrix approximation in column sum norm"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "lower bounds for approximation schemes for closest string"
    - "on low rank approximation of binary matrices"
    - "sketching as a tool for numerical linear algebra"
    - "randomized algorithms for matrices and data"
    - "on the closest string and substring problems"

Connected through hyperedge HE-3 (induced by source paper "streaming ptas for binary ell_0 low rank approximation"):
  with members:
    - "streaming ptas for constrained k means"
    - "a unified framework for clustering constrained data without locality property"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "generalized independent component analysis over finite alphabets"
    - "faster algorithms for the constrained k means problem"
    - "low rank approximation of binary matrices column subset selection and generalizations"
    - "a simple d 2 sampling based ptas for k means and other clustering problems"

Connected through hyperedge HE-4 (induced by source paper "parameterized low rank binary matrix approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"
    - "generalized independent component analysis over finite alphabets"
    - "fast biclustering by dual parameterization"
    - "sketching as a tool for numerical linear algebra"
    - "np hardness of hypercube 2 segmentation"

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)