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: "obstacle tower a generalization challenge in vision control and planning"
  Abstract: The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-3 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "torchbeast a pytorch platform for distributed rl"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (induced by source paper "increasing generality in machine learning through procedural content generation"):
  with members:
    - "fully differentiable procedural content generation through generative playing networks"
    - "pcgrl procedural content generation via reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "procedural content generation through quality diversity"
    - "arena a general evaluation platform and building toolkit for multi agent intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-5 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors a retrospective"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "deep reinforcement learning from policy dependent human feedback"
    - "hierarchical deep q network with forgetting from imperfect demonstrations in minecraft"
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "string diagrams for assembly planning"
    - "improving deep reinforcement learning in minecraft with action advice"
    - "keeping your distance solving sparse reward tasks using self balancing shaped rewards"

Connected through hyperedge HE-6 (induced by source paper "rotation translation and cropping for zero shot generalization"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "deep reinforcement learning for general video game ai"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "natural environment benchmarks for reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"

Connected through hyperedge HE-7 (induced by source paper "tanksworld a multi agent environment for ai safety research"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "on the complexity of reconnaissance blind chess"
    - "ai safety gridworlds"
    - "dart noise injection for robust imitation learning"
    - "concrete problems in ai safety"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "meta learning of sequential strategies"
    - "grunge a grand unified atp challenge"
    - "analysing mathematical reasoning abilities of neural models"
    - "graph representations for higher order logic and theorem proving"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"

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)