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 task programming learning to generalize across hierarchical tasks"
  Abstract: In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.

Connected through hyperedge HE-1 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "6 dof grasping for target driven object manipulation in clutter"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize across long horizon tasks from human demonstrations"):
  with members:
    - "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"
    - "learning deep parameterized skills from demonstration for re targetable visuomotor control"
    - "learning latent plans from play"
    - "deep local trajectory replanning and control for robot navigation"
    - "avid learning multi stage tasks via pixel level translation of human videos"
    - "scaling robot supervision to hundreds of hours with roboturk robotic manipulation dataset through human reasoning and dexterity"
    - "learning to optimize join queries with deep reinforcement learning"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"

Connected through hyperedge HE-3 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives"):
  with members:
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "object centric forward modeling for model predictive control"
    - "good robot efficient reinforcement learning for multi step visual tasks via reward shaping"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "universal planning networks"
    - "reasoning about physical interactions with object oriented prediction and planning"

Connected through hyperedge HE-4 (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"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"

Connected through hyperedge HE-6 (induced by source paper "squirl robust and efficient learning from video demonstration of long horizon robotic manipulation tasks"):
  with members:
    - "meta inverse reinforcement learning with probabilistic context variables"
    - "learning latent plans from play"
    - "a divergence minimization perspective on imitation learning methods"
    - "motion reasoning for goal based imitation learning"
    - "deepmind control suite"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-7 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-8 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta 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.AI(Artificial Intelligence)