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: "many goals reinforcement learning"
  Abstract: All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representations and where all possible goals could be explicitly enumerated and learned separately. In this paper we empirically explore 3 different extensions of the idea of updating many (instead of all) goals in the context of RL with deep neural networks (or DeepRL for short). First, in a direct adaptation of Kaelbling's approach we explore if many-goals updating can be used to achieve mastery in non-tabular visual-observation domains. Second, we explore whether many-goals updating can be used to pre-train a network to subsequently learn faster and better on a single main task of interest. Third, we explore whether many-goals updating can be used to provide auxiliary task updates in training a network to learn faster and better on a single main task of interest. We provide comparisons to baselines for each of the 3 extensions.

Connected through hyperedge HE-1 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "time limits in reinforcement learning"
    - "action branching architectures for deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "value iteration networks"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "planning with goal conditioned policies"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "diagnosing and enhancing vae models"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "general non linear bellman equations"
    - "on inductive biases in deep reinforcement learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-5 (induced by source paper "skew fit state covering self supervised reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "temporal difference models model free deep rl for model based control"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-6 (induced by source paper "hindsight trust region policy optimization"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hierarchical imitation and reinforcement learning"
    - "hindsight experience replay"

Connected through hyperedge HE-7 (induced by source paper "clic curriculum learning and imitation for object control in non rewarding environments"):
  with members:
    - "learning actionable representations with goal conditioned policies"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "large scale study of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning and exploiting multiple subgoals for fast exploration in hierarchical reinforcement learning"):
  with members:
    - "data efficient hierarchical reinforcement learning"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "feudal networks for hierarchical reinforcement learning"
    - "feature control as intrinsic motivation for hierarchical reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

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)