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: "active inverse reward design"
  Abstract: Reward design, the problem of selecting an appropriate reward function for an AI system, is both critically important, as it encodes the task the system should perform, and challenging, as it requires reasoning about and understanding the agent's environment in detail. As a result, system designers often iterate on the reward function in a trial-and-error process to get their desired behavior. We propose structuring this process as a series of reward design queries, where we actively select the set of reward functions available to the designer. We query with two types of sets: discrete queries, where the system designer chooses from a small set of reward functions, and feature queries, where the system queries the designer for weights on a small subset of features. After each query, we use inverse reward design (IRD) (Hadfield-Menell et al., 2017) to update the distribution over the true reward function from the observed proxy reward function chosen by the designer. Compared to vanilla IRD, we find that our approach not only decreases the uncertainty about the true reward, but also greatly improves performance in unseen environments while only querying for reward functions in a single training environment.

Connected through hyperedge HE-1 (induced by source paper "reward rational implicit choice a unifying formalism for reward learning"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "preferences implicit in the state of the world"
    - "inverse reward design"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "the off switch game"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning a prior over intent via meta inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "safe exploration in continuous action spaces"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "reward learning from human preferences and demonstrations in atari"

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)