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: "parameterized low rank binary matrix approximation"
  Abstract: We provide a number of algorithmic results for the following family of problems: For a given binary m\times n matrix A and integer k, decide whether there is a "simple" binary matrix B which differs from A in at most k entries. For an integer r, the "simplicity" of B is characterized as follows. #R##N#- Binary r-Means: Matrix B has at most r different columns. This problem is known to be NP-complete already for r=2. We show that the problem is solvable in time 2^{O(k\log k)}\cdot(nm)^{O(1)} and thus is fixed-parameter tractable parameterized by k. We prove that the problem admits a polynomial kernel when parameterized by r and k but it has no polynomial kernel when parameterized by k only unless NP\subseteq coNP/poly. We also complement these result by showing that when being parameterized by r and k, the problem admits an algorithm of running time 2^{O(r\cdot \sqrt{k\log{(k+r)}})}(nm)^{O(1)}, which is subexponential in k for r\in O(k^{1/2 -\epsilon}) for any \epsilon>0. #R##N#- Low GF(2)-Rank Approximation: Matrix B is of GF(2)-rank at most r. This problem is known to be NP-complete already for r=1. It also known to be W[1]-hard when parameterized by k. Interestingly, when parameterized by r and k, the problem is not only fixed-parameter tractable, but it is solvable in time 2^{O(r^{ 3/2}\cdot \sqrt{k\log{k}})}(nm)^{O(1)}, which is subexponential in k. #R##N#- Low Boolean-Rank Approximation: Matrix B is of Boolean rank at most r. The problem is known to be NP-complete for k=0 as well as for r=1. We show that it is solvable in subexponential in k time 2^{O(r2^r\cdot \sqrt{k\log k})}(nm)^{O(1)}.

Connected through hyperedge HE-1 (induced by source paper "low rank approximation from communication complexity"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "relative error tensor low rank approximation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank passthrough neural networks"
    - "greedy bi criteria approximations for k medians and k means"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"

Connected through hyperedge HE-2 (induced by source paper "on clustering incomplete data"):
  with members:
    - "parameterized k clustering the distance matters"
    - "approximation schemes for low rank binary matrix approximation problems"
    - "parameterized algorithms for the matrix completion problem"
    - "approximation algorithms for stochastic clustering"
    - "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-3 (induced by source paper "low rank binary matrix approximation in column sum norm"):
  with members:
    - "approximation schemes for low rank binary matrix approximation problems"
    - "on the complexity of robust pca and l1 norm low rank 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-4 (induced by source paper "approximation schemes for low rank binary matrix approximation problems"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"
    - "generalized independent component analysis over finite alphabets"
    - "sketching as a tool for numerical linear algebra"
    - "computing a nonnegative matrix factorization provably"
    - "randomized algorithms for matrices and data"

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)