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: "efficient autonomy validation in simulation with adaptive stress testing"
  Abstract: During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep reinforcement learning (DRL) solver to identify likely failure scenarios. However, the solver's use of a feed-forward neural network with a discretized space of possible initial conditions poses two major problems. First, the system is not treated as a black box, in that it requires analyzing the internal state of the system, which leads to considerable implementation complexities. Second, in order to simulate realistic settings, a new instance of the solver needs to be run for each initial condition. Running a new solver for each initial condition not only significantly increases the computational complexity, but also disregards the underlying relationship between similar initial conditions. We provide a solution to both problems by employing a recurrent neural network that takes a set of initial conditions from a continuous space as input. This approach enables robust and efficient detection of failures because the solution generalizes across the entire space of initial conditions. By simulating an instance where an autonomous car drives while a pedestrian is crossing a road, we demonstrate the solver is now capable of finding solutions for problems that would have previously been intractable.

Connected through hyperedge HE-1 (induced by source paper "validation of image based neural network controllers through adaptive stress testing"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "reachability analysis and safety verification for neural network control systems"
    - "efficient formal safety analysis of neural networks"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards proving the adversarial robustness of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "learning quadrotor dynamics using neural network for flight control"

Connected through hyperedge HE-2 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "interpreting the latent space of gans for semantic face editing"
    - "conditional generative neural system for probabilistic trajectory prediction"

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.LG(Machine Learning)