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: "on low rank approximation of binary matrices"
  Abstract: We consider the problem of low rank approximation of binary matrices. Here we are given a $d \times n$ binary matrix $A$ and a small integer $k < d$. The goal is to find two binary matrices $U$ and $V$ of sizes $d \times k$ and $k \times n$ respectively, so that the Frobenius norm of $A-U V$ is minimized. There are two models of this problem, depending on the definition of the product of binary matrices: The $\mathrm{GF}(2)$ model and the Boolean semiring model. Previously, the only known results are $2$-approximation algorithms for the special case $k=1$ \cite{KDD:ShenJY09, Jiang14} (where the two models are equivalent). #R##N#In this paper, we give the first results for the general case $k>1$ for both $\mathrm{GF}(2)$ and Boolean model. For the $\mathrm{GF}(2)$ model, we show that a simple column-selection algorithm achieves $O(k)$-approximation. For the Boolean model, we develop a new algorithm and show that it is $O(2^k)$-approximation. For constant $k$, both algorithms run in polynomial time in the size of the matrix. We also show that the low rank binary matrix approximation problem is NP-hard even for $k=1$, solving a conjecture in \cite{Koyuturk03}.

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"
    - "parameterized low rank binary 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"

Connected through hyperedge HE-2 (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"
    - "parameterized low rank binary matrix approximation"
    - "lower bounds for approximation schemes for closest string"
    - "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 "parameterized low rank binary matrix approximation"):
  with members:
    - "approximation schemes for low rank binary matrix approximation problems"
    - "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"
    - "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"

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"
    - "parameterized low rank binary matrix approximation"
    - "input sparsity and hardness for robust subspace approximation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "algorithms for ell_p low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "sketching as a tool for numerical linear algebra"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"
    - "algorithms and hardness for subspace approximation"

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.CC(Computational Complexity)