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: "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
  Abstract: Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are rarely used in practice on safety-critical systems, since the learned policy typically does not yield any safety guarantees. That is, the required exploration may cause physical harm to the robot or its environment. In this paper, we present a method to learn accurate safety certificates for nonlinear, closed-loop dynamical systems. Specifically, we construct a neural network Lyapunov function and a training algorithm that adapts it to the shape of the largest safe region in the state space. The algorithm relies only on knowledge of inputs and outputs of the dynamics, rather than on any specific model structure. We demonstrate our method by learning the safe region of attraction for a simulated inverted pendulum. Furthermore, we discuss how our method can be used in safe learning algorithms together with statistical models of dynamical systems.

Connected through hyperedge HE-1 (induced by source paper "learning stable deep dynamics models"):
  with members:
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "a lyapunov based approach to safe reinforcement learning"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous deep q learning with model based acceleration"
    - "input convex neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "synthesis of control barrier functions using a supervised machine learning approach"):
  with members:
    - "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"
    - "control barrier functions theory and applications"
    - "an optimal task allocation strategy for heterogeneous multi robot systems"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "control of multi agent systems with finite time control barrier certificates and temporal logic"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-3 (induced by source paper "safe wasserstein constrained deep q learning"):
  with members:
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "constrained policy optimization"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "safe exploration of state and action spaces in reinforcement learning"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-4 (induced by source paper "manga method agnostic neural policy generalization and adaptation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "exploration by random network distillation"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-5 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems"):
  with members:
    - "stable gaussian process based tracking control of euler lagrange systems"
    - "a control lyapunov perspective on episodic learning via projection to state stability"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "neural lander stable drone landing control using learned dynamics"
    - "sim to real learning agile locomotion for quadruped robots"
    - "a lyapunov based approach to safe reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "safe learning of quadrotor dynamics using barrier certificates"

Connected through hyperedge HE-6 (induced by source paper "verification of neural network control policy under persistent adversarial perturbation"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "fast neural network verification via shadow prices"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "a lyapunov based approach to safe reinforcement learning"
    - "reachability analysis and safety verification for neural network control systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

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)