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 for inverse reinforcement learning algorithms and applications"
  Abstract: Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand of the minimum set of demonstrations needed to teach a specific sequential decision-making task. We formalize the problem of finding optimal demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing an equivalence to the set cover problem, and use this equivalence to develop an efficient algorithm for determining the set of maximally-informative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: benchmarking active learning IRL algorithms and developing an IRL algorithm that, rather than assuming demonstrations are i.i.d., uses counterfactual reasoning over informative demonstrations to learn more efficiently.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"

Connected through hyperedge HE-3 (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"
    - "an overview of machine teaching"
    - "teaching categories to human learners with visual explanations"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "an overview of machine teaching"
    - "repeated inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "policy poisoning in batch reinforcement learning and control"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "data poisoning attacks on stochastic bandits"
    - "online data poisoning attack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "data poisoning attacks against online learning"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-6 (induced by source paper "using causal analysis to learn specifications from task demonstrations"):
  with members:
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "counterfactual multi agent policy gradients"
    - "unsupervised learning of disentangled representations from video"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "learning trajectory preferences for manipulators via iterative improvement"

Connected through hyperedge HE-7 (induced by source paper "exploring computational user models for agent policy summarization"):
  with members:
    - "discovering blind spots in reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "establishing appropriate trust via critical states"
    - "explanation in artificial intelligence insights from the social sciences"
    - "enabling robots to communicate their objectives"
    - "visualizing and understanding atari agents"

Connected through hyperedge HE-8 (induced by source paper "class teaching for inverse reinforcement learners"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "machine teaching for bayesian learners in the exponential family"
    - "multi armed bandits for intelligent tutoring 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.LG(Machine Learning)