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: "dynamical systems on weighted lattices general theory"
  Abstract: In this work a theory is developed for unifying large classes of nonlinear discrete-time dynamical systems obeying a superposition of a weighted maximum or minimum type. The state vectors and input-output signals evolve on nonlinear spaces which we call complete weighted lattices and include as special cases the nonlinear vector spaces of minimax algebra. Their algebraic structure has a polygonal geometry. Some of the special cases unified include max-plus, max-product, and probabilistic dynamical systems. We study problems of representation in state and input-output spaces using lattice monotone operators, state and output responses using nonlinear convolutions, solving nonlinear matrix equations using lattice adjunctions, stability and controllability. We outline applications in state-space modeling of nonlinear filtering; dynamic programming (Viterbi algorithm) and shortest paths (distance maps); fuzzy Markov chains; and tracking audio-visual salient events in multimodal information streams using generalized hidden Markov models with control inputs.

Connected through hyperedge HE-1 (induced by source paper "tropical polynomial division and neural networks"):
  with members:
    - "a universal approximation result for difference of log sum exp neural networks"
    - "tropical geometry of deep neural networks"
    - "log sum exp neural networks and posynomial models for convex and log log convex data"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "tropical geometry and piecewise linear approximation of curves and surfaces on weighted lattices"):
  with members:
    - "a universal approximation result for difference of log sum exp neural networks"
    - "tropical geometry of deep neural networks"
    - "log sum exp neural networks and posynomial models for convex and log log convex data"
    - "ensemble methods for convex regression with applications to geometric programming based circuit design"

Connected through hyperedge HE-3 (induced by source paper "an adaptive pruning algorithm for spoofing localisation based on tropical geometry"):
  with members:
    - "information theory a tutorial introduction"

Connected through hyperedge HE-4 (induced by source paper "entropy operates in non linear semifields"):
  with members:
    - "the case for shifting the renyi entropy"

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.SY(Systems and Control)