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: "near optimal representation learning for hierarchical reinforcement learning"
  Abstract: We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial. To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation. We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice. Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods (see videos at this https URL).

Connected through hyperedge HE-1 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "dynamics aware unsupervised discovery of skills"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "hierarchical reinforcement learning with advantage based auxiliary rewards"
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "skill transfer in deep reinforcement learning under morphological heterogeneity"
    - "hardware conditioned policies for multi robot transfer learning"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "value propagation networks"
    - "learning multi level hierarchies with hindsight"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "on variational bounds of mutual information"
    - "prediction consistency curvature representation learning for locally linear control"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-4 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-5 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "visual reinforcement learning with imagined goals"
    - "bleu is not suitable for the evaluation of text simplification"

Connected through hyperedge HE-6 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "hierarchical policy learning is sensitive to goal space design"
    - "interpretable neural predictions with differentiable binary variables"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"

Connected through hyperedge HE-7 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "kickstarting deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning latent plans from play"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "behavioral cloning from observation"
    - "self supervised visual planning with temporal skip connections"

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)