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 graphs generalizing to unseen tasks from a single video demonstration"
  Abstract: Our goal is to generate a policy to complete an unseen task given just a single video demonstration of the task in a given domain. We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model. To this end, we propose Neural Task Graph (NTG) Networks, which use conjugate task graph as the intermediate representation to modularize both the video demonstration and the derived policy. We empirically show NTG achieves inter-task generalization on two complex tasks: Block Stacking in BulletPhysics and Object Collection in AI2-THOR. NTG improves data efficiency with visual input as well as achieve strong generalization without the need for dense hierarchical supervision. We further show that similar performance trends hold when applied to real-world data. We show that NTG can effectively predict task structure on the JIGSAWS surgical dataset and generalize to unseen tasks.

Connected through hyperedge HE-1 (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"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"

Connected through hyperedge HE-2 (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-3 (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"
    - "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-4 (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"
    - "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"
    - "learning generalized reactive policies using deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "scalable multi task imitation learning with autonomous improvement"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "soft actor critic algorithms and applications"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "hindsight experience replay"
    - "one shot visual imitation learning via meta learning"

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"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "videograph recognizing minutes long human activities in videos"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "video time properties encoders and evaluation"
    - "video action transformer network"
    - "timeception for complex action recognition"

Connected through hyperedge HE-8 (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"

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.CV(Computer Vision and Pattern Recognition)