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 the complexity of computing grobner bases for weighted homogeneous systems"
  Abstract: Solving polynomial systems arising from applications is frequently made easier by the structure of the systems. Weighted homogeneity (or quasi-homogeneity) is one example of such a structure: given a system of weights W = ( w 1 , ? , w n ) , W-homogeneous polynomials are polynomials which are homogeneous w.r.t. the weighted degree deg W ? ( X 1 α 1 ? X n α n ) = ? w i α i .Grobner bases for weighted homogeneous systems can be computed by adapting existing algorithms for homogeneous systems to the weighted homogeneous case. We show that in this case, the complexity estimate for Algorithm F 5 ( ( n + d max - 1 d max ) ω ) can be divided by a factor ( ? w i ) ω . For zero-dimensional systems, the complexity of Algorithm FGLM n D ω (where D is the number of solutions of the system) can be divided by the same factor ( ? w i ) ω . Under genericity assumptions, for zero-dimensional weighted homogeneous systems of W-degree ( d 1 , ? , d n ) , these complexity estimates are polynomial in the weighted Bezout bound ? i = 1 n d i / ? i = 1 n w i .Furthermore, the maximum degree reached in a run of Algorithm F 5 is bounded by the weighted Macaulay bound ? ( d i - w i ) + w n , and this bound is sharp if we can order the weights so that w n = 1 . For overdetermined semi-regular systems, estimates from the homogeneous case can be adapted to the weighted case.We provide some experimental results based on systems arising from a cryptography problem and from polynomial inversion problems. They show that taking advantage of the weighted homogeneous structure can yield substantial speed-ups, and allows us to solve systems which were otherwise out of reach.

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)