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: "linear identification of nonlinear systems a lifting technique based on the koopman operator"
  Abstract: We exploit the key idea that nonlinear system identification is equivalent to linear identification of the socalled Koopman operator. Instead of considering nonlinear system identification in the state space, we obtain a novel linear identification technique by recasting the problem in the infinite-dimensional space of observables. This technique can be described in two main steps. In the first step, similar to the socalled Extended Dynamic Mode Decomposition algorithm, the data are lifted to the infinite-dimensional space and used for linear identification of the Koopman operator. In the second step, the obtained Koopman operator is "projected back" to the finite-dimensional state space, and identified to the nonlinear vector field through a linear least squares problem. The proposed technique is efficient to recover (polynomial) vector fields of different classes of systems, including unstable, chaotic, and open systems. In addition, it is robust to noise, well-suited to model low sampling rate datasets, and able to infer network topology and dynamics.

Connected through hyperedge HE-1 (induced by source paper "koopman based control of a soft continuum manipulator under variable loading conditions"):
  with members:
    - "comparison and experimental validation of predictive models for soft fiber reinforced actuators"
    - "active learning of dynamics for data driven control using koopman operators"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "model based control using koopman operators"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

Connected through hyperedge HE-3 (induced by source paper "learning compositional koopman operators for model based control"):
  with members:
    - "deep variational koopman models inferring koopman observations for uncertainty aware dynamics modeling and control"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "graph networks as learnable physics engines for inference and control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

Connected through hyperedge HE-4 (induced by source paper "topology inference over networks with nonlinear coupling"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "social learning over weakly connected graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "efficiently learning ising models on arbitrary graphs"
    - "directed information graphs"

Connected through hyperedge HE-5 (induced by source paper "modeling and control of soft robots using the koopman operator and model predictive control"):
  with members:
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "model based control using koopman operators"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"

Connected through hyperedge HE-6 (induced by source paper "local tomography of large networks under the low observability regime"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-7 (induced by source paper "nonlinear system identification of soft robot dynamics using koopman operator theory"):
  with members:
    - "force generation by parallel combinations of fiber reinforced fluid driven actuators"
    - "model based control using koopman operators"

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)