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: "optimizing agent behavior over long time scales by transporting value"
  Abstract: Humans spend a remarkable fraction of waking life engaged in acts of "mental time travel". We dwell on our actions in the past and experience satisfaction or regret. More than merely autobiographical storytelling, we use these event recollections to change how we will act in similar scenarios in the future. This process endows us with a computationally important ability to link actions and consequences across long spans of time, which figures prominently in addressing the problem of long-term temporal credit assignment; in artificial intelligence (AI) this is the question of how to evaluate the utility of the actions within a long-duration behavioral sequence leading to success or failure in a task. Existing approaches to shorter-term credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a new paradigm for reinforcement learning where agents use recall of specific memories to credit actions from the past, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire computational models in neuroscience, psychology, and behavioral economics.

Connected through hyperedge HE-1 (induced by source paper "finding online neural update rules by learning to remember"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "dota 2 with large scale deep reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "large scale adversarial representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "metalearned neural memory"
    - "does learning require memorization a short tale about a long tail"
    - "network of evolvable neural units evolving to learn at a synaptic level"

Connected through hyperedge HE-2 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-3 (induced by source paper "challenges of real world reinforcement learning"):
  with members:
    - "value constrained model free continuous control"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reward constrained policy optimization"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-4 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "trellis networks for sequence modeling"
    - "can recurrent neural networks warp time"

Connected through hyperedge HE-5 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-6 (induced by source paper "self attentional credit assignment for transfer in reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "learning to generalize from sparse and underspecified rewards"
    - "reward shaping via meta learning"
    - "deep contextualized word representations"
    - "kickstarting deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "rudder return decomposition for delayed rewards"

Connected through hyperedge HE-7 (induced by source paper "sequence modeling of temporal credit assignment for episodic reinforcement learning"):
  with members:
    - "generative adversarial self imitation learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evolved policy gradients"
    - "rudder return decomposition for delayed rewards"
    - "hindsight experience replay"
    - "molecular de novo design through deep reinforcement learning"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "constraint satisfaction propagation non stationary policy synthesis for temporal logic planning"):
  with members:
    - "temporal logic control of general markov decision processes by approximate policy refinement"
    - "rudder return decomposition for delayed rewards"
    - "variational option discovery algorithms"
    - "eigenoption discovery through the deep successor representation"
    - "the eigenoption critic framework"
    - "hierarchical subtask discovery with non negative matrix factorization"
    - "environment independent task specifications via gltl"
    - "the option critic architecture"

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)