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: "from below approximations in boolean matrix factorization geometry and new algorithm"
  Abstract: Abstract   We present new results on Boolean matrix factorization and a new algorithm based on these results. The results emphasize the significance of factorizations that provide from-below approximations of the input matrix. While the previously proposed algorithms do not consider the possibly different significance of different matrix entries, our results help measure such significance and suggest where to focus when computing factors. An experimental evaluation of the new algorithm on both synthetic and real data demonstrates its good performance in terms of good coverage by the first few factors as well as a small number of factors needed for an almost exact decomposition and indicates that the algorithm outperforms the available ones in these terms. We also propose future research topics.

Connected through hyperedge HE-1 (induced by source paper "from below boolean matrix factorization algorithm based on mdl"):
  with members:
    - "what is the dimension of your binary data"

Connected through hyperedge HE-2 (induced by source paper "multimodal clustering for community detection"):
  with members:
    - "algorithms for graph constrained coalition formation in the real world"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "towards a closure operator for enumeration of maximal tricliques in tripartite hypergraphs"
    - "towards a unified taxonomy of biclustering methods"
    - "clustering boolean tensors"
    - "introduction to formal concept analysis and its applications in information retrieval and related fields"
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "concept stability for constructing taxonomies of web site users"

Connected through hyperedge HE-3 (induced by source paper "what you will gain by rounding theory and algorithms for rounding rank"):
  with members:
    - "hyperbolae are no hyperbole modelling communities that are not cliques"
    - "the complexity of computing the minimum rank of a sign pattern matrix"
    - "what is the dimension of your binary data"

Connected through hyperedge HE-4 (induced by source paper "improved algorithms for exact and approximate boolean matrix decomposition"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "on the complexity of nonnegative matrix factorization"
    - "relative error cur matrix decompositions"

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.NA(Numerical Analysis)