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: "using program induction to interpret transition system dynamics"
  Abstract: Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions. We propose to learn high level functional programs in order to represent abstract models which capture the invariant structure in the observed data. We introduce the $\pi$-machine (program-induction machine) -- an architecture able to induce interpretable LISP-like programs from observed data traces. We propose an optimisation procedure for program learning based on backpropagation, gradient descent and A* search. We apply the proposed method to two problems: system identification of dynamical systems and explaining the behaviour of a DQN agent. Our results show that the $\pi$-machine can efficiently induce interpretable programs from individual data traces.

Connected through hyperedge HE-1 (induced by source paper "learning programmatically structured representations with perceptor gradients"):
  with members:
    - "towards symbolic reinforcement learning with common sense"
    - "isolating sources of disentanglement in variational autoencoders"
    - "programmatically interpretable reinforcement learning"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "deep reinforcement learning a brief survey"
    - "learning to infer graphics programs from hand drawn images"
    - "beating atari with natural language guided reinforcement learning"
    - "differentiable programs with neural libraries"

Connected through hyperedge HE-2 (induced by source paper "a categorisation of post hoc explanations for predictive models"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "consistent individualized feature attribution for tree ensembles"
    - "bayesian patchworks an approach to case based reasoning"
    - "global model interpretation via recursive partitioning"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "developing bug free machine learning systems with formal mathematics"

Connected through hyperedge HE-3 (induced by source paper "the orca hub explainable offshore robotics through intelligent interfaces"):
  with members:
    - "miriam a multimodal chat based interface for autonomous systems"

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.AI(Artificial Intelligence)