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: "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
  Abstract: This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loop. The goal is to achieve a unity map between the desired and the actual outputs. In previous work, the efficacy of this approach was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed DNN approach achieved an average impromptu tracking error reduction of 43% as compared to the baseline feedback controller. Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture. In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases the training efficiency can be further increased.

Connected through hyperedge HE-1 (induced by source paper "catch the ball accurate high speed motions for mobile manipulators via inverse dynamics learning"):
  with members:
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-2 (induced by source paper "knowledge transfer between robots with similar dynamics for high accuracy impromptu trajectory tracking"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "one shot visual imitation learning via meta learning"
    - "multi robot transfer learning a dynamical system perspective"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning transferable policies for monocular reactive mav control"
    - "distributed iterative learning control for a team of quadrotors"
    - "learning modular neural network policies for multi task and multi robot transfer"

Connected through hyperedge HE-3 (induced by source paper "an analysis of the expressiveness of deep neural network architectures based on their lipschitz constants"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "large scale model predictive control with neural networks and primal active sets"
    - "neural lander stable drone landing control using learned dynamics"
    - "bounding and counting linear regions of deep neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "going deeper with convolutions"
    - "deep speech scaling up end to end speech recognition"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-4 (induced by source paper "online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge"):
  with members:
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
    - "learning quadrotor dynamics using neural network for flight control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-5 (induced by source paper "neural lander stable drone landing control using learned dynamics"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning quadrotor dynamics using neural network for flight control"
    - "safe controller optimization for quadrotors with gaussian processes"

Connected through hyperedge HE-6 (induced by source paper "data efficient multirobot multitask transfer learning for trajectory tracking"):
  with members:
    - "adapt zero shot adaptive policy transfer for stochastic dynamical systems"
    - "multi robot transfer learning a dynamical system perspective"
    - "one shot imitation learning"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-7 (induced by source paper "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"):
  with members:
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

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.RO(Robotics)