human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an inductive synthesis framework for verifiable reinforcement learning"):
  Abstract: Despite the tremendous advances that have been made in the last decade on developing useful machine-learning applications, their wider adoption has been hindered by the lack of strong assurance guarantees that can be made about their behavior. In this paper, we consider how formal verification techniques developed for traditional software systems can be repurposed for verification of reinforcement learning-enabled ones, a particularly important class of machine learning systems. Rather than enforcing safety by examining and altering the structure of a complex neural network implementation, our technique uses blackbox methods to synthesizes deterministic programs, simpler, more interpretable, approximations of the network that can nonetheless guarantee desired safety properties are preserved, even when the network is deployed in unanticipated or previously unobserved environments. Our methodology frames the problem of neural network verification in terms of a counterexample and syntax-guided inductive synthesis procedure over these programs. The synthesis procedure searches for both a deterministic program and an inductive invariant over an infinite state transition system that represents a specification of an application's control logic. Additional specifications defining environment-based constraints can also be provided to further refine the search space. Synthesized programs deployed in conjunction with a neural network implementation dynamically enforce safety conditions by monitoring and preventing potentially unsafe actions proposed by neural policies. Experimental results over a wide range of cyber-physical applications demonstrate that software-inspired formal verification techniques can be used to realize trustworthy reinforcement learning systems with low overhead.

Includes members:
  Member "concolic testing for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "importance driven deep learning system testing")
  Member "programmatically interpretable reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning")
  Member "reachability analysis of deep neural networks with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "verifiable reinforcement learning via policy extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "feature guided black box safety testing of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "importance driven deep learning system testing")
  Member "deep reinforcement learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low rank bottleneck in multi head attention models")
  Member "towards generalization and simplicity in continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "reluplex an efficient smt solver for verifying deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

Question: Please tell me which class the source paper 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)