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: "adapting behaviour via intrinsic reward a survey and empirical study"
  Abstract: Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience---how to adapt the system's behaviour---to optimize the learning of a collection of value functions. A simple answer is to compute an intrinsic reward based on the statistics of each auxiliary learner, and use reinforcement learning to maximize that intrinsic reward. Unfortunately, implementing this simple idea has proven difficult, and thus has been the focus of decades of study. It remains unclear which of the many possible measures of learning would work well in a parallel learning setting where environmental reward is extremely sparse or absent. In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed. We discuss the interaction between reward and prediction learners and highlight the importance of introspective prediction learners: those that increase their rate of learning when progress is possible, and decrease when it is not. We provide a comprehensive empirical comparison of 15 different rewards, including well-known ideas from reinforcement learning and active learning. Our results highlight a simple but seemingly powerful principle: intrinsic rewards based on the amount of learning can generate useful behaviour, if each individual learner is introspective.

Connected through hyperedge HE-1 (induced by source paper "what s a good prediction issues in evaluating general value functions through error"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "when is a prediction knowledge"
    - "general value function networks"
    - "a first empirical study of emphatic temporal difference learning"
    - "introspective agents confidence measures for general value functions"
    - "temporal difference networks"
    - "multi timescale nexting in a reinforcement learning robot"

Connected through hyperedge HE-2 (induced by source paper "adapting behaviour for learning progress"):
  with members:
    - "experience replay optimization"
    - "on inductive biases in deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "what can learned intrinsic rewards capture"):
  with members:
    - "meta learning via learned loss"
    - "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"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "one shot visual imitation learning via meta 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)