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: "energy scaling of targeted optimal control of complex networks"
  Abstract: Recently it has been shown that the control energy required to control a dynamical complex network is prohibitively large when there are only a few control inputs. Most methods to reduce the control energy have focused on where, in the network, to place additional control inputs. Here, in contrast, we show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We validate our conclusions in model and real networks to arrive at an energy scaling law to better design control objectives regardless of system size, energy restrictions, state restrictions, input node choices and target node choices. The energy required to control a dynamical complex network can be prohibitively large when there are only a few control inputs. Here the authors demonstrate that if only a subset of the network is targeted the energy requirements decrease exponentially.

Connected through hyperedge HE-1 (induced by source paper "functional target controllability of networks structural properties and efficient algorithms"):
  with members:
    - "minimum energy control for complex networks"
    - "the observability radius of networks"
    - "gramian based reachability metrics for bilinear networks"
    - "controllability metrics limitations and algorithms for complex networks"

Connected through hyperedge HE-2 (induced by source paper "controllability gramian submatrices for a network consensus model"):
  with members:
    - "sign patterns of inverse doubly nonnegative matrices"
    - "controllability of networked mimo systems"
    - "controllability metrics limitations and algorithms for complex networks"

Connected through hyperedge HE-3 (induced by source paper "control energy of lattice graphs"):
  with members:
    - "control principles of complex networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "controllability metrics limitations and algorithms for complex networks"

Connected through hyperedge HE-4 (induced by source paper "target control of directed networks based on network flow problems"):
  with members:
    - "control principles of complex networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "maximum matchings and minimum dominating sets in apollonian networks and extended tower of hanoi graphs"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "maximum matchings in scale free networks with identical degree distribution"
    - "domination number and minimum dominating sets in pseudofractal scale free web and sierpinski graph"
    - "control principles of complex networks"
    - "efficient sub 5 approximations for minimum dominating sets in unit disk graphs"

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)