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: "playing fps games with deep reinforcement learning"
  Abstract: Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as humans in deathmatch scenarios.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-2 (induced by source paper "visual hide and seek"):
  with members:
    - "two body problem collaborative visual task completion"
    - "emergent tool use from multi agent autocurricula"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-3 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "embodied multimodal multitask learning"):
  with members:
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "compositional attention networks for machine reasoning"
    - "the sound of pixels"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "beating the world s best at super smash bros with deep reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-6 (induced by source paper "drlviz understanding decisions and memory in deep reinforcement learning"):
  with members:
    - "learning 3d navigation protocols on touch interfaces with cooperative multi agent reinforcement learning"
    - "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "lstm a search space odyssey"
    - "deep reinforcement learning a brief survey"
    - "deep learning for video game playing"
    - "neural map structured memory for deep reinforcement learning"
    - "iqa visual question answering in interactive environments"

Connected through hyperedge HE-7 (induced by source paper "human level performance in 3d multiplayer games with population based reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "population based training of neural networks"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "neural modular control for embodied question answering"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "deep reinforcement learning that matters"
    - "home a household multimodal environment"

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)