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: "generative adversarial imitation learning"
  Abstract: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"

Connected through hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-6 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "dynamics aware unsupervised discovery of skills"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "hierarchical reinforcement learning with advantage based auxiliary rewards"
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "skill transfer in deep reinforcement learning under morphological heterogeneity"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "hardware conditioned policies for multi robot transfer learning"

Connected through hyperedge HE-7 (induced by source paper "generalized hindsight for reinforcement learning"):
  with members:
    - "sub policy adaptation for hierarchical reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deep drone racing learning agile flight in dynamic environments"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"

Connected through hyperedge HE-8 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

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)