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: "an overview of machine teaching"
  Abstract: In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can be characterized in this space. We hope this organization allows us to gain deeper understanding of individual teaching problems, discover connections among them, and identify gaps in the field.

Connected through hyperedge HE-1 (induced by source paper "crowdsourcing the perception of machine teaching"):
  with members:
    - "diversity in machine learning"
    - "the challenge of crafting intelligible intelligence"
    - "trick me if you can human in the loop generation of adversarial examples for question answering"
    - "meta transfer learning for few shot learning"
    - "understanding hand object manipulation by modeling the contextual relationship between actions grasp types and object attributes"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "machine teaching a new paradigm for building machine learning systems"
    - "becoming the expert interactive multi class machine teaching"

Connected through hyperedge HE-2 (induced by source paper "machine education designing semantically ordered and ontologically guided modular neural networks"):
  with members:
    - "machine teaching in hierarchical genetic reinforcement learning curriculum design of reward functions for swarm shepherding"
    - "transparent machine education of neural networks for swarm shepherding using curriculum design"

Connected through hyperedge HE-3 (induced by source paper "single episode policy transfer in reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "fingerprint policy optimisation for robust reinforcement learning"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "decoupling dynamics and reward for transfer learning"
    - "learning to explore with meta policy gradient"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-4 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "teaching categories to human learners with visual explanations"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on educating machines"):
  with members:
    - "machine teaching in hierarchical genetic reinforcement learning curriculum design of reward functions for swarm shepherding"
    - "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-6 (induced by source paper "interactive teaching algorithms for inverse reinforcement learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "dual policy iteration"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning to collaborate in markov decision processes"):
  with members:
    - "modeling others using oneself in multi agent reinforcement learning"
    - "online reinforcement learning in stochastic games"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "corralling a band of bandit algorithms"
    - "prediction with a short memory"
    - "fast convergence of regularized learning in games"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-8 (induced by source paper "optimal collusion free teaching"):
  with members:
    - "unlabeled sample compression schemes and corner peelings for ample and maximum classes"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"
    - "preference based teaching"
    - "teaching and compressing for low vc dimension"
    - "sample compression schemes for vc classes"

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)