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: "real root finding for rank defects in linear hankel matrices"
  Abstract: Let $H\_0, ..., H\_n$ be $m \times m$ matrices with entries in $\QQ$ and Hankel structure, i.e. constant skew diagonals. We consider the linear Hankel matrix $H(\vecx)=H\_0+\X\_1H\_1+...+\X\_nH\_n$ and the problem of computing sample points in each connected component of the real algebraic set defined by the rank constraint ${\sf rank}(H(\vecx))\leq r$, for a given integer $r \leq m-1$. Computing sample points in real algebraic sets defined by rank defects in linear matrices is a general problem that finds applications in many areas such as control theory, computational geometry, optimization, etc. Moreover, Hankel matrices appear in many areas of engineering sciences. Also, since Hankel matrices are symmetric, any algorithmic development for this problem can be seen as a first step towards a dedicated exact algorithm for solving semi-definite programming problems, i.e. linear matrix inequalities. Under some genericity assumptions on the input (such as smoothness of an incidence variety), we design a probabilistic algorithm for tackling this problem. It is an adaptation of the so-called critical point method that takes advantage of the special structure of the problem. Its complexity reflects this: it is essentially quadratic in specific degree bounds on an incidence variety. We report on practical experiments and analyze how the algorithm takes advantage of this special structure. A first implementation outperforms existing implementations for computing sample points in general real algebraic sets: it tackles examples that are out of reach of the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "determinantal sets singularities and application to optimal control in medical imagery"):
  with members:
    - "data discriminants of likelihood equations"
    - "real root finding for low rank linear matrices"
    - "real root finding for determinants of linear matrices"
    - "critical points and gr obner bases the unmixed case"
    - "on the complexity of the generalized minrank problem"

Connected through hyperedge HE-2 (induced by source paper "solving rank constrained semidefinite programs in exact arithmetic"):
  with members:
    - "real root finding for low rank linear matrices"
    - "real root finding for determinants of linear matrices"
    - "a nearly optimal algorithm for deciding connectivity queries in smooth and bounded real algebraic sets"
    - "probabilistic algorithm for polynomial optimization over a real algebraic set"

Connected through hyperedge HE-3 (induced by source paper "real root finding for low rank linear matrices"):
  with members:
    - "sparse fglm algorithms"
    - "determinantal sets singularities and application to optimal control in medical imagery"
    - "bit complexity for multi homogeneous polynomial system solving application to polynomial minimization"
    - "real root finding for determinants of linear matrices"
    - "polynomial systems solving by fast linear algebra"
    - "a nearly optimal algorithm for deciding connectivity queries in smooth and bounded real algebraic sets"
    - "probabilistic algorithm for polynomial optimization over a real algebraic set"
    - "critical points and gr obner bases the unmixed case"

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.SC(Symbolic Computation)