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: "infogail interpretable imitation learning from visual demonstrations"
  Abstract: The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (induced by source paper "domain adversarial and conditional state space model for imitation learning"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "learning belief representations for imitation learning in pomdps"
    - "learning robotic manipulation through visual planning and acting"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "recent advances in imitation learning from observation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-5 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-6 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"):
  with members:
    - "distilling policy distillation"
    - "learning novel policies for tasks"
    - "ranking based reward extrapolation without rankings"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "dibs diversity inducing information bottleneck in model ensembles"):
  with members:
    - "mim mutual information machine"
    - "variational adversarial active learning"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "learning not to learn training deep neural networks with biased data"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "attention based ensemble for deep metric learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-8 (induced by source paper "loss annealed gail for sample efficient and stable imitation learning"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "rethinking imagenet pre training"
    - "addressing function approximation error in actor critic methods"
    - "learning goal embeddings via self play for hierarchical reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "asynchronous methods for deep reinforcement learning"

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)