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: "neural predictive belief representations"
  Abstract: Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far. In this paper, we investigate whether it is possible to learn such a belief representation using modern neural architectures. Specifically, we focus on one-step frame prediction and two variants of contrastive predictive coding (CPC) as the objective functions to learn the representations. To evaluate these learned representations, we test how well they can predict various pieces of information about the underlying state of the environment, e.g., position of the agent in a 3D maze. We show that all three methods are able to learn belief representations of the environment, they encode not only the state information, but also its uncertainty, a crucial aspect of belief states. We also find that for CPC multi-step predictions and action-conditioning are critical for accurate belief representations in visually complex environments. The ability of neural representations to capture the belief information has the potential to spur new advances for learning and planning in partially observable domains, where leveraging uncertainty is essential for optimal decision making.

Connected through hyperedge HE-1 (induced by source paper "dream to control learning behaviors by latent imagination"):
  with members:
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "exploring model based planning with policy networks"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "meta reinforcement learning as task inference"):
  with members:
    - "guided meta policy search"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "emergent coordination through competition"
    - "deepmind control suite"
    - "neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning dexterous in hand manipulation"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"):
  with members:
    - "categorizing wireheading in partially embedded agents"
    - "model based reinforcement learning for atari"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"
    - "supervising strong learners by amplifying weak experts"

Connected through hyperedge HE-5 (induced by source paper "causally correct partial models for reinforcement learning"):
  with members:
    - "causal discovery with reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "combating the compounding error problem with a multi step model"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "when to trust your model model based policy optimization"
    - "shaping belief states with generative environment models for rl"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-6 (induced by source paper "learning belief representations for imitation learning in pomdps"):
  with members:
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "recurrent world models facilitate policy evolution"
    - "representation learning with contrastive predictive coding"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-7 (induced by source paper "world discovery models"):
  with members:
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "observe and look further achieving consistent performance on atari"
    - "model based active exploration"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "no representation without transformation"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "unsupervised learning of object keypoints for perception and control"
    - "dynamics aware unsupervised discovery of skills"
    - "temporal cycle consistency learning"
    - "shaping belief states with generative environment models for rl"
    - "neural processes"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "towards a definition of disentangled representations"

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)