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: "sparse bayesian inference of multivariable arx networks"
  Abstract: Increasing attention has recently been given to the inference of sparse networks. In biology, for example, most molecules only bind to a small number of other molecules, leading to sparse molecular interaction networks. To achieve sparseness, a common approach consists of applying weighted penalties to the number of links between nodes in the network and the complexity of the dynamics of existing links. The selection of proper weights, however, is non-trivial. Alternatively, this paper proposes a novel data-driven method, called GESBL, that is able to penalise both network sparsity and model complexity without any tuning. GESBL combines Sparse Bayesian Learning (SBL) and Group Sparse Bayesian Learning (GSBL) to introduce penalties for complexity, both in terms of element (system order of nonzero connections) and group sparsity (network topology). The paper considers a class of sparse linear time-invariant networks where the dynamics are represented by multivariable ARX models. Data generated from sparse random ARX networks and synthetic gene regulatory networks indicate that our method, on average, considerably outperforms existing state-of-the-art methods. The proposed method can be applied to a wide range of fields, from systems biology applications in signalling and genetic regulatory networks to power systems.

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)