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: "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
  Abstract: Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.

Connected through hyperedge HE-1 (induced by source paper "strips action discovery"):
  with members:
    - "learning strips action models with classical planning"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "learning strips operators from noisy and incomplete observations"
    - "learning partially observable deterministic action models"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "alfred a benchmark for interpreting grounded instructions for everyday tasks"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning to navigate unseen environments back translation with environmental dropout"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning robotic manipulation through visual planning and acting"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "model ensemble trust region policy optimization"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"
    - "spectral normalization for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"

Connected through hyperedge HE-5 (induced by source paper "hallucinative topological memory for zero shot visual planning"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep neuroevolution of recurrent and discrete world models"):
  with members:
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "evolving deep neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-7 (induced by source paper "joint learning of unsupervised object based perception and control"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised learning of object keypoints for perception and control"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "multi object representation learning with iterative variational inference"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "strategic object oriented reinforcement learning"
    - "relational deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "regression planning networks"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "deep object centric policies for autonomous driving"
    - "making neural programming architectures generalize via recursion"

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)