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: "schema networks zero shot transfer with a generative causal model of intuitive physics"
  Abstract: The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. Nonetheless, progress on task-to-task transfer remains limited. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-3 (induced by source paper "generalized hindsight for reinforcement learning"):
  with members:
    - "sub policy adaptation for hierarchical reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deep drone racing learning agile flight in dynamic environments"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "investigating simple object representations in model free deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "counterfactuals uncover the modular structure of deep generative models"
    - "on relaxing determinism in arithmetic circuits"
    - "budgeted experiment design for causal structure learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "adapting to unseen environments through explicit representation of context"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "meta learning by the baldwin effect"
    - "learning curriculum policies for reinforcement learning"
    - "automated curriculum learning by rewarding temporally rare events"
    - "lstm a search space odyssey"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

Connected through hyperedge HE-8 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

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)