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: "reward shaping via meta learning"
  Abstract: Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple similar tasks to solve. In this paper, we consider reward shaping on a distribution of tasks, and propose a general meta-learning framework to automatically learn the efficient reward shaping on newly sampled tasks, assuming only shared state space but not necessarily action space. We first derive the theoretically optimal reward shaping in terms of credit assignment in model-free RL. We then propose a value-based meta-learning algorithm to extract an effective prior over the optimal reward shaping. The prior can be applied directly to new tasks, or provably adapted to the task-posterior while solving the task within few gradient updates. We demonstrate the effectiveness of our shaping through significantly improved learning efficiency and interpretable visualizations across various settings, including notably a successful transfer from DQN to DDPG.

Connected through hyperedge HE-1 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "hyper meta reinforcement learning with sparse reward"):
  with members:
    - "meta reinforcement learning with task embedding and shared policy"
    - "meta inverse reinforcement learning with probabilistic context variables"
    - "dealing with sparse rewards in reinforcement learning"
    - "mame model agnostic meta exploration"
    - "norml no reward meta learning"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "learning to adapt meta learning for model based control"

Connected through hyperedge HE-3 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "learning to teach with dynamic loss functions"

Connected through hyperedge HE-4 (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"
    - "optimizing agent behavior over long time scales by transporting value"
    - "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-5 (induced by source paper "predictive coding for boosting deep reinforcement learning with sparse rewards"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "learning montezuma s revenge from a single demonstration"
    - "archer aggressive rewards to counter bias in hindsight experience replay"

Connected through hyperedge HE-6 (induced by source paper "meta learning via learned loss"):
  with members:
    - "guided meta policy search"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "generalized inner loop meta learning"
    - "meta gradient reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "evolved policy gradients"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-7 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "long range indoor navigation with prm rl"
    - "deep visual mpc policy learning for navigation"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "credit assignment as a proxy for transfer in reinforcement learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "generating long sequences with sparse transformers"
    - "multi task deep reinforcement learning with popart"
    - "assessing generalization in deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rudder return decomposition for delayed rewards"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)