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: "unsupervised grounding of plannable first order logic representation from images"
  Abstract: Recently, there is an increasing interest in obtaining the relational structures of the environment in the Reinforcement Learning community. However, the resulting "relations" are not the discrete, logical predicates compatible to the symbolic reasoning such as classical planning or goal recognition. Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between deep-learning perceptual systems and symbolic classical planners. One key component of the system is a Neural Network called State AutoEncoder (SAE), which encodes an image-based input into a propositional representation compatible to classical planning. To get the best of both worlds, we propose First-Order State AutoEncoder, an unsupervised architecture for grounding the first-order logic predicates and facts. Each predicate models a relationship between objects by taking the interpretable arguments and returning a propositional value. In the experiment using 8-Puzzle and a photo-realistic Blocksworld environment, we show that (1) the resulting predicates capture the interpretable relations (e.g. spatial), (2) they help obtaining the compact, abstract model of the environment, and finally, (3) the resulting model is compatible to symbolic classical planning.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "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"
    - "composable planning with attributes"
    - "combined reinforcement learning via abstract representations"

Connected through hyperedge HE-2 (induced by source paper "making sense of sensory input"):
  with members:
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "composable planning with attributes"
    - "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-3 (induced by source paper "hallucinative topological memory for zero shot visual planning"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "model based reinforcement learning for atari"
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-4 (induced by source paper "disentangled relational representations for explaining and learning from demonstration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "multi object representation learning with iterative variational inference"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "interpretable latent spaces for learning from demonstration"
    - "isolating sources of disentanglement in variational autoencoders"
    - "unsupervised learning of disentangled representations from video"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "an explicitly relational neural network architecture"):
  with members:
    - "neural logic machines"
    - "universal transformers"
    - "relational recurrent neural networks"
    - "deep learning a critical appraisal"
    - "learning explanatory rules from noisy data"
    - "non local neural networks"
    - "attention is all you need"
    - "recurrent relational networks"

Connected through hyperedge HE-6 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "lstm based goal recognition in latent space"

Connected through hyperedge HE-7 (induced by source paper "learning first order symbolic representations for planning from the structure of the state space"):
  with members:
    - "model free model based and general intelligence"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "combined reinforcement learning via abstract representations"
    - "transfer of deep reactive policies for mdp planning"
    - "learning features and abstract actions for computing generalized plans"
    - "deep learning a critical appraisal"
    - "human level intelligence or animal like abilities"

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)