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 "physics informed topology learning in networks of linear dynamical systems"):
  Abstract: Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings.

Includes members:
  Member "data driven identification of a thermal network in multi zone building":
    also connected through other hyperedges:
      (none)
  Member "learning the exact topology of undirected consensus networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data driven identification of a thermal network in multi zone building")
  Member "network inference from consensus dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "graph learning from data under laplacian and structural constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "topology identification of directed dynamical networks via power spectral analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks")
  Member "directed information graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variable lag granger causality and transfer entropy for time series analysis")
  Member "consistency of the group lasso and multiple kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint user selection and precoding in multiuser mimo systems via group lasso")

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)