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: "changing the environment based on empowerment as intrinsic motivation"
  Abstract: One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent’s environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment.

Connected through hyperedge HE-1 (induced by source paper "influence based multi agent exploration"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "exploration by random network distillation"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "minerl a large scale dataset of minecraft demonstrations"):
  with members:
    - "learning dexterous in hand manipulation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the atari grand challenge dataset"
    - "interactive 3d modeling with a generative adversarial network"
    - "hierarchical and interpretable skill acquisition in multi task reinforcement learning"
    - "stardata a starcraft ai research dataset"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-3 (induced by source paper "computational theories of curiosity driven learning"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "policy search in continuous action domains an overview"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "reverse curriculum generation for reinforcement learning"
    - "count based exploration with neural density models"
    - "the intentional unintentional agent learning to solve many continuous control tasks simultaneously"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of goal spaces for intrinsically motivated goal exploration"):
  with members:
    - "independently controllable factors"
    - "reverse curriculum generation for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "vime variational information maximizing exploration"
    - "adam a method for stochastic optimization"
    - "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable problem"

Connected through hyperedge HE-5 (induced by source paper "accelerating empowerment computation with uct tree search"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "empowerment an introduction"

Connected through hyperedge HE-6 (induced by source paper "generative design in minecraft gdmc settlement generation competition"):
  with members:
    - "general video game rule generation"

Connected through hyperedge HE-7 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "direct ensemble estimation of density functionals"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of mutual information"
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mutual information for discrete continuous mixtures"
    - "ensemble estimation of information divergence"

Connected through hyperedge HE-8 (induced by source paper "ensemble estimation of mutual information"):
  with members:
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "demystifying fixed k nearest neighbor information estimators"
    - "a review of feature selection methods based on mutual information"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"

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)