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: "explaining transition systems through program induction"
  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 three problems: system identification of dynamical systems, explaining the behaviour of a DQN agent and learning by demonstration in a human-robot interaction scenario. Our experimental results show that the $\pi$-machine can efficiently induce interpretable programs from individual data traces.

Connected through hyperedge HE-1 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "context specific independence in bayesian networks"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-2 (induced by source paper "from explanation to synthesis compositional program induction for learning from demonstration"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "specification patterns for robotic missions"
    - "learning programmatically structured representations with perceptor gradients"
    - "deep drone racing learning agile flight in dynamic environments"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "programmatically interpretable reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-3 (induced by source paper "abstracting probabilistic relational models"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "bisimulation based approximate lifted inference"
    - "solving sat and bayesian inference with backtracking search"
    - "representation dependence in probabilistic inference"
    - "an abstract monte carlo method for the analysis of probabilistic programs"

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)