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: "the atari grand challenge dataset"
  Abstract: Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is to augment RL with learning from human demonstrations. However, human demonstration data is not yet readily available. This hinders progress in this direction. The present work addresses this problem as follows. We (i) collect and describe a large dataset of human Atari 2600 replays -- the largest and most diverse such data set publicly released to date, (ii) illustrate an example use of this dataset by analyzing the relation between demonstration quality and imitation learning performance, and (iii) outline possible research directions that are opened up by our work.

Connected through hyperedge HE-1 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "this looks like that deep learning for interpretable image recognition"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "learning calibratable policies using programmatic style consistency"):
  with members:
    - "deepmind control suite"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "adversarial information factorization"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-3 (induced by source paper "pre training with non expert human demonstration for deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "observe and look further achieving consistent performance on atari"
    - "deep reinforcement learning for doom using unsupervised auxiliary tasks"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"
    - "distral robust multitask reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "atari head atari human eye tracking and demonstration dataset"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "minerl a large scale dataset of minecraft demonstrations"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "free lunch saliency via attention in atari agents"
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "agil learning attention from human for visuomotor tasks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "using natural language for reward shaping in reinforcement learning"):
  with members:
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to understand goal specifications by modelling reward"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "minerl a large scale dataset of minecraft demonstrations"):
  with members:
    - "learning dexterous in hand manipulation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "interactive 3d modeling with a generative adversarial network"
    - "hierarchical and interpretable skill acquisition in multi task reinforcement learning"
    - "stardata a starcraft ai research dataset"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-7 (induced by source paper "pre training in deep reinforcement learning for automatic speech recognition"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "multi task semi supervised adversarial autoencoding for speech emotion"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "emorl continuous acoustic emotion classification using deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-8 (induced by source paper "pre training neural networks with human demonstrations for deep reinforcement learning"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "the arcade learning environment an evaluation platform for general agents"

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)