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: "deep abstract q networks"
  Abstract: We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for existing methods. Methods using abstraction (Dietterich 2000; Sutton, Precup, and Singh 1999) have shown to be useful in tackling long-horizon problems. We combine recent techniques of deep reinforcement learning with existing model-based approaches using an expert-provided state abstraction. We construct toy domains that elucidate the problem of long horizons, sparse rewards and high-dimensional inputs, and show that our algorithm significantly outperforms previous methods on these domains. Our abstraction-based approach outperforms Deep Q-Networks (Mnih et al. 2015) on Montezuma's Revenge and Venture, and exhibits backtracking behavior that is absent from previous methods.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-3 (induced by source paper "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning powerful policies by using consistent dynamics model"
    - "mega reward achieving human level play without extrinsic rewards"
    - "model based reinforcement learning for atari"
    - "strategic object oriented reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-4 (induced by source paper "uncertainty sensitive learning and planning with ensembles"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "model based reinforcement learning for atari"
    - "adaptive online planning for continual lifelong learning"
    - "an investigation of model free planning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "single agent policy tree search with guarantees"
    - "self imitation learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-5 (induced by source paper "exploration with unreliable intrinsic reward in multi agent reinforcement learning"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "is q learning provably efficient"
    - "counterfactual multi agent policy gradients"
    - "parameter space noise for exploration"
    - "the uncertainty bellman equation and exploration"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "playing atari ball games with hierarchical reinforcement learning"):
  with members:
    - "learning abstract options"
    - "hierarchical policy design for sample efficient learning of robot table tennis through self play"
    - "hierarchical imitation and reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "investigating human priors for playing video games"
    - "efficient exploration through bayesian deep q networks"
    - "rainbow combining improvements in deep reinforcement learning"
    - "count based exploration with neural density models"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "noisy networks for exploration"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-8 (induced by source paper "hierarchical imitation and reinforcement learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "exploration exploitation in mdps with options"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "generating long term trajectories using deep hierarchical networks"

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)