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: "empowerment for continuous agent environment systems"
  Abstract: This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning.

Connected through hyperedge HE-1 (induced by source paper "analyzing visual representations in embodied navigation tasks"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "the replica dataset a digital replica of indoor spaces"
    - "dd ppo learning near perfect pointgoal navigators from 2 5 billion frames"
    - "exploration by random network distillation"
    - "group normalization"
    - "on evaluation of embodied navigation agents"
    - "a study on overfitting in deep reinforcement learning"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-2 (induced by source paper "neural embedding for physical manipulations"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "normalized diversification"
    - "interpretable latent spaces for learning from demonstration"
    - "hierarchical visuomotor control of humanoids"
    - "spectral normalization for generative adversarial networks"
    - "latent space policies for hierarchical reinforcement learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "the journey is the reward unsupervised learning of influential trajectories"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "a unified bellman equation for causal information and value in markov decision processes"
    - "when the map is better than the territory"
    - "proximal policy optimization algorithms"
    - "variational intrinsic control"
    - "empowerment an introduction"

Connected through hyperedge HE-5 (induced by source paper "diversity is all you need learning skills without a reward function"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep reinforcement learning that matters"
    - "equivalence between policy gradients and soft q learning"
    - "meta learning shared hierarchies"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "ddco discovery of deep continuous options for robot learning from demonstrations"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "curiosity driven reinforcement learning with homeostatic regulation"):
  with members:
    - "a unified bellman equation for causal information and value in markov decision processes"
    - "curiosity driven exploration by self supervised prediction"
    - "vime variational information maximizing exploration"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "unifying count based exploration and intrinsic motivation"
    - "continuous control with deep reinforcement learning"
    - "planning to be surprised optimal bayesian exploration in dynamic environments"

Connected through hyperedge HE-7 (induced by source paper "changing the environment based on empowerment as intrinsic motivation"):
  with members:
    - "empowerment an introduction"
    - "quantifying morphological computation"
    - "reinforcement learning a survey"

Connected through hyperedge HE-8 (induced by source paper "information driven self organization of complex robotic behaviors"):
  with members:
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"
    - "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"

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)