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 minerl competition on sample efficient reinforcement learning using human priors"
  Abstract: Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. #R##N#The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. #R##N#Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.

Connected through hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "self supervised learning of image embedding for continuous control"
    - "momentum contrast for unsupervised visual representation learning"
    - "learning latent plans from play"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-2 (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"
    - "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"
    - "obstacle tower a generalization challenge in vision control and planning"

Connected through hyperedge HE-3 (induced by source paper "sample efficient reinforcement learning through learning from demonstrations in minecraft"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "experience replay for continual learning"
    - "self imitation learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "hierarchical deep q network from imperfect demonstrations in minecraft"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "reinforcement learning from imperfect demonstrations"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-5 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "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"
    - "obstacle tower a generalization challenge in vision control and planning"

Connected through hyperedge HE-6 (induced by source paper "on the measure of intelligence"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "quantifying generalization in reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-7 (induced by source paper "dota 2 with large scale deep reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "growing a brain fine tuning by increasing model capacity"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an empirical model of large batch training"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "craftassist a framework for dialogue enabled interactive agents"):
  with members:
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "habitat a platform for embodied ai research"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "talk the walk navigating new york city through grounded dialogue"
    - "deep reinforcement learning with model learning and monte carlo tree search in minecraft"
    - "exploring the limits of weakly supervised pretraining"

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.LG(Machine Learning)