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: "observational learning by reinforcement learning"
  Abstract: Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the effect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a 'teacher' (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent's behaviour. The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour.

Connected through hyperedge HE-1 (induced by source paper "mutual information based state control for intrinsically motivated reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "dynamical distance learning for semi supervised and unsupervised skill discovery"
    - "efficient dialog policy learning via positive memory retention"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"

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"
    - "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-3 (induced by source paper "reinforced imitation in heterogeneous action space"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "internal model from observations for reward shaping"
    - "playing hard exploration games by watching youtube"
    - "zero shot visual imitation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-4 (induced by source paper "hybrid reinforcement learning with expert state sequences"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "eigenoption discovery through the deep successor representation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "visual question generation as dual task of visual question answering"
    - "third person imitation learning"

Connected through hyperedge HE-5 (induced by source paper "avoidance learning using observational reinforcement learning"):
  with members:
    - "count based exploration with neural density models"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "conditional image generation with pixelcnn decoders"
    - "unifying count based exploration and intrinsic motivation"
    - "high dimensional continuous control using generalized advantage estimation"
    - "algorithms for cvar optimization in mdps"

Connected through hyperedge HE-6 (induced by source paper "what deep learning can tell us about higher cognitive functions like mindreading"):
  with members:
    - "third person imitation learning"
    - "learning multiagent communication with backpropagation"
    - "deep predictive coding networks"

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)