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: "exploratory gradient boosting for reinforcement learning in complex domains"
  Abstract: High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for learning on $Q$-function residuals. And second, we propose an exploration strategy inspired by the principles of state abstraction and information acquisition under uncertainty. We demonstrate the empirical effectiveness of these techniques, first, as a preliminary check, on two standard tasks (Blackjack and $n$-Chain), and then on two much larger and more realistic tasks with high-dimensional observation spaces. Specifically, we introduce two benchmarks built within the game Minecraft where the observations are pixel arrays of the agent's visual field. A combination of our two algorithmic techniques performs competitively on the standard reinforcement-learning tasks while consistently and substantially outperforming baselines on the two tasks with high-dimensional observation spaces. The new function approximator, exploration strategy, and evaluation benchmarks are each of independent interest in the pursuit of reinforcement-learning methods that scale to real-world domains.

Connected through hyperedge HE-1 (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-2 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning"):
  with members:
    - "population guided parallel policy search for reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "knowledge distillation by on the fly native ensemble"
    - "off policy deep reinforcement learning without exploration"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning to walk via deep reinforcement learning"
    - "deep mutual learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-6 (induced by source paper "clustered reinforcement learning"):
  with members:
    - "exploration by random network distillation"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-7 (induced by source paper "improving deep reinforcement learning in minecraft with action advice"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "newtonian action advice integrating human verbal instruction with reinforcement learning"
    - "sfv reinforcement learning of physical skills from videos"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "one shot imitation learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-8 (induced by source paper "orrb openai remote rendering backend"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "learning dexterous in hand manipulation"
    - "unity a general platform for intelligent agents"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "cad2rl real single image flight without a single real image"

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)