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: "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
  Abstract: We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies. Unlike existing hierarchical multitask RL approaches that explicitly describe what the agent should do at a high level, our problem only describes properties of subtasks and relationships among them, which requires the agent to perform complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural subtask graph solver (NSGS) which encodes the subtask graph using a recursive neural network embedding. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy, graph reward propagation, to pre-train our NSGS agent and further finetune it through actor-critic method. The experimental results on two 2D visual domains show that our agent can perform complex reasoning to find a near-optimal way of executing the subtask graph and generalize well to the unseen subtask graphs. In addition, we compare our agent with a Monte-Carlo tree search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of NSGS can be further improved by combining it with MCTS.

Connected through hyperedge HE-1 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies"):
  with members:
    - "neural logic machines"
    - "on first order meta learning algorithms"
    - "bayesian model agnostic meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "probabilistic model agnostic meta learning"
    - "learning explanatory rules from noisy data"
    - "a deep compositional framework for human like language acquisition in virtual environment"

Connected through hyperedge HE-2 (induced by source paper "plan arithmetic compositional plan vectors for multi task control"):
  with members:
    - "hierarchical visuomotor control of humanoids"
    - "composable deep reinforcement learning for robotic manipulation"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "latent space policies for hierarchical reinforcement learning"
    - "task embedded control networks for few shot imitation learning"
    - "neural probabilistic motor primitives for humanoid control"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-3 (induced by source paper "regression planning networks"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"
    - "deep object centric policies for autonomous driving"
    - "making neural programming architectures generalize via recursion"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-4 (induced by source paper "learning to plan hierarchically from curriculum"):
  with members:
    - "action categorization for computationally improved task learning and planning"
    - "independently controllable features"
    - "automatic goal generation for reinforcement learning agents"
    - "independently controllable factors"
    - "despot online pomdp planning with regularization"
    - "playing atari with deep reinforcement learning"

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)