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: "composable planning with attributes"
  Abstract: The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning how its actions affect those properties, it may be able to use this knowledge to solve complex tasks without training specifically for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a method that learns a policy for transitioning between "nearby" sets of attributes, and maintains a graph of possible transitions. Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan. We show in 3D block stacking, grid-world games, and StarCraft that our model is able to generalize to longer, more complex tasks at test time by composing simpler learned policies.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "combined reinforcement learning via abstract representations"

Connected through hyperedge HE-2 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"

Connected through hyperedge HE-3 (induced by source paper "making sense of sensory input"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "semi supervised online structure learning for composite event recognition"
    - "flexible neural representation for physics prediction"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "structured agents for physical construction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "surprising negative results for generative adversarial tree search"
    - "graph networks as learnable physics engines for inference and control"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "unity a general platform for intelligent agents"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "mapping state space using landmarks for universal goal reaching"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "semi parametric topological memory for navigation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-6 (induced by source paper "plots procedure learning from observations using subtask structure"):
  with members:
    - "reinforcement learning on web interfaces using workflow guided exploration"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "ddco discovery of deep continuous options for robot learning from demonstrations"

Connected through hyperedge HE-7 (induced by source paper "learning portable representations for high level planning"):
  with members:
    - "active exploration for learning symbolic representations"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "the thing that we tried didn t work very well deictic representation in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning and planning with a semantic model"):
  with members:
    - "model free model based and general intelligence"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to navigate in cities without a map"
    - "self supervised visual planning with temporal skip connections"
    - "feudal networks for hierarchical reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural map structured memory for deep reinforcement learning"
    - "rl 2 fast reinforcement learning via slow 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.AI(Artificial Intelligence)