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: "learning a prior over intent via meta inverse reinforcement learning"
  Abstract: A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a "prior" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.

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"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "repeated inverse reinforcement learning"
    - "game theoretic modeling of human adaptation in human robot collaboration"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "end to end robotic reinforcement learning without reward engineering"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "learning to interactively learn and assist"):
  with members:
    - "deep reinforcement learning from policy dependent human feedback"
    - "online meta learning"
    - "risk aware active inverse reinforcement learning"
    - "interactive learning with corrective feedback for policies based on deep neural networks"
    - "active inverse reward design"
    - "reward learning from human preferences and demonstrations in atari"
    - "task embedded control networks for few shot imitation learning"
    - "hg dagger interactive imitation learning with human experts"

Connected through hyperedge HE-5 (induced by source paper "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning to optimize neural nets"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-6 (induced by source paper "meta reinforcement learning with task embedding and shared policy"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "learning to explore with meta policy gradient"
    - "evolved policy gradients"
    - "learning to adapt meta learning for model based control"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-7 (induced by source paper "what can learned intrinsic rewards capture"):
  with members:
    - "meta learning via learned loss"
    - "adapting behaviour via intrinsic reward a survey and empirical study"
    - "meta gradient reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "meta learning update rules for unsupervised representation learning"
    - "learning to explore with meta policy gradient"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-8 (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"
    - "ai safety gridworlds"
    - "repeated inverse 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.LG(Machine Learning)