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: "approximating the permanent of a random matrix with vanishing mean"
  Abstract: Building upon a recent approach pioneered by Barvinok [4, 5, 7, 8] we present a quasi-polynomial time algorithm for approximating the permanent of a typical n x n random matrix with unit variance and vanishing mean {\mu} = (ln ln n)-1/6 to within inverse polynomial multiplicative error. This result counters the common intuition that the difficulty of computing the permanent, even approximately, stems merely from our inability to treat matrices with many opposing signs. We believe that our approach may have several implications to understanding the permanent in the context of computational complexity, anti-concentration statistics, and de-randomization.

Connected through hyperedge HE-1 (induced by source paper "a deterministic algorithm for counting colorings with 2 delta colors"):
  with members:
    - "algorithms for bis hard problems on expander graphs"
    - "zeros of holant problems locations and algorithms"
    - "algorithmic pirogov sinai theory"
    - "improved bounds for randomly sampling colorings via linear programming"
    - "the ising partition function zeros and deterministic approximation"
    - "an fptas for counting proper four colorings on cubic graphs"

Connected through hyperedge HE-2 (induced by source paper "approximating the determinant of well conditioned matrices by shallow circuits"):
  with members:
    - "the ising partition function zeros and deterministic approximation"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "a tight analysis of bethe approximation for permanent"):
  with members:
    - "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"
    - "approximate profile maximum likelihood"
    - "belief propagation bethe approximation and polynomials"
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "nash social welfare matrix permanent and stable polynomials"
    - "the bethe permanent of a nonnegative matrix"
    - "approximating the permanent with fractional belief propagation"
    - "approximating the permanent with belief propagation"

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)