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: "machine teaching in hierarchical genetic reinforcement learning curriculum design of reward functions for swarm shepherding"
  Abstract: The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a human to design the reward function or to allow a human to transfer the skills developed in designing reward functions to another human and in a systematic manner. In this paper, we use Systematic Instructional Design, an approach in human education, to engineer a machine education methodology to design reward functions for reinforcement learning. We demonstrate the methodology in designing a hierarchical genetic reinforcement learner that adopts a neural network representation to evolve a swarm controller for an agent shepherding a boids-based swarm. The results reveal that the methodology is able to guide the design of hierarchical reinforcement learners, with each model in the hierarchy learning incrementally through a multi-part reward function. The hierarchy acts as a decision fusion function that combines the individual behaviours and skills learnt by each instruction to create a smart shepherd to control the swarm.

Connected through hyperedge HE-1 (induced by source paper "machine education designing semantically ordered and ontologically guided modular neural networks"):
  with members:
    - "transparent machine education of neural networks for swarm shepherding using curriculum design"
    - "an overview of machine teaching"

Connected through hyperedge HE-2 (induced by source paper "on educating machines"):
  with members:
    - "an overview of machine teaching"
    - "progressive neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "a multi disciplinary review of knowledge acquisition methods"
    - "computational red teaming in a sudoku solving context neural network based skill representation and acquisition"
    - "a review of theoretical and practical challenges of trusted autonomy in big data"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive review of shepherding as a bio inspired swarm robotics guidance approach"):
  with members:
    - "machine education designing semantically ordered and ontologically guided modular neural networks"
    - "transparent machine education of neural networks for swarm shepherding using curriculum design"
    - "lifelong testing of smart autonomous systems by shepherding a swarm of watchdog artificial intelligence agents"
    - "networking the boids is more robust against adversarial learning"

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)