human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dynamic network reconstruction from heterogeneous datasets"):
  Abstract: Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations to parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that underlying networks share the same Boolean structure across all experiments. ARMAX models are used as an example to parametrize linear network models, known as "dynamical structure functions" (DSFs), which describe causal interactions between measured variables. Multiple datasets are integrated in one regression problem with additional demands of group sparsity to assure network sparsity and structure consistency. To perform group sparse estimation, we introduce and extend the iterative reweighted l1 method (with ADMM implementation), sparse Bayesian learning and sampling-based methods. Numerical examples illustrate the performance in random tests, which benchmark the proposed methods for stable ARX networks and DSF models. In summary, this paper presents an efficient network reconstruction method that takes advantage of all available data from multiple experiments, rather than processing datasets separately.

Includes members:
  Member "inverse problems for matrix exponential in system identification system aliasing":
    also connected through other hyperedges:
      (none)
  Member "systems aliasing in dynamic network reconstruction issues on low sampling frequencies":
    also connected through other hyperedges:
      (none)
  Member "identifying biochemical reaction networks from heterogeneous datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse bayesian inference of multivariable arx networks")

Question: Please tell me which class the source paper 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)