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: "repeated inverse reinforcement learning"
  Abstract: We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.

Connected through hyperedge HE-1 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"):
  with members:
    - "distilling policy distillation"
    - "learning novel policies for tasks"
    - "ranking based reward extrapolation without rankings"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "game theoretic modeling of human adaptation in human robot collaboration"

Connected through hyperedge HE-2 (induced by source paper "preferences implicit in the state of the world"):
  with members:
    - "imitating latent policies from observation"
    - "penalizing side effects using stepwise relative reachability"
    - "measuring and avoiding side effects using relative reachability"
    - "visual reinforcement learning with imagined goals"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to understand goal specifications by modelling reward"
    - "one shot imitation from observing humans via domain adaptive meta 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"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

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"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "generative adversarial imitation learning"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "inverse reinforcement learning in contextual mdps"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "introduction to online convex optimization"
    - "truly batch apprenticeship learning with deep successor features"
    - "contextual markov decision processes using generalized linear models"
    - "average reward reinforcement learning with unknown mixing times"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "ai safety gridworlds"

Connected through hyperedge HE-6 (induced by source paper "learning parametric constraints in high dimensions from demonstrations"):
  with members:
    - "predictive modeling by infinite horizon constrained inverse optimal control with application to a human manipulation task"
    - "learning constraints from demonstrations"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "risk sensitive inverse reinforcement learning via semi and non parametric methods"
    - "safe exploration in finite markov decision processes with gaussian processes"

Connected through hyperedge HE-7 (induced by source paper "multi task maximum entropy inverse reinforcement learning"):
  with members:
    - "multi agent generative adversarial imitation learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "robust imitation of diverse behaviors"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "one shot imitation learning"
    - "generative adversarial imitation learning"
    - "unrolled generative adversarial networks"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-8 (induced by source paper "active inverse reward design"):
  with members:
    - "learning dexterous in hand manipulation"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"
    - "deep bayesian active learning with image data"
    - "inverse reward design"
    - "concrete problems in ai safety"
    - "value iteration networks"
    - "april active preference learning based reinforcement 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)