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: "imitation learning as f divergence minimization"
  Abstract: We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes. Our key insight is to minimize the right divergence between the learner and the expert state-action distributions, namely the reverse KL divergence or I-projection. We propose a general imitation learning framework for estimating and minimizing any f-Divergence. By plugging in different divergences, we are able to recover existing algorithms such as Behavior Cloning (Kullback-Leibler), GAIL (Jensen Shannon) and Dagger (Total Variation). Empirical results show that our approximate I-projection technique is able to imitate multi-modal behaviors more reliably than GAIL and behavior cloning.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "minimax weight and q function learning for off policy evaluation"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "algaedice policy gradient from arbitrary experience"
    - "a divergence minimization perspective on imitation learning methods"
    - "exponential family estimation via adversarial dynamics embedding"

Connected through hyperedge HE-2 (induced by source paper "state alignment based imitation learning"):
  with members:
    - "wasserstein adversarial imitation learning"
    - "provably efficient imitation learning from observation alone"
    - "efficient exploration via state marginal matching"
    - "imitating latent policies from observation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-3 (induced by source paper "imitation learning via off policy distribution matching"):
  with members:
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "large scale gan training for high fidelity natural image synthesis"
    - "mine mutual information neural estimation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "a divergence minimization perspective on imitation learning methods"):
  with members:
    - "efficient exploration via state marginal matching"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "formal limitations on the measurement of mutual information"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "dynamic regret convergence analysis and an adaptive regularization algorithm for on policy robot imitation learning"):
  with members:
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "an algorithmic perspective on imitation learning"
    - "lipschitz continuity in model based reinforcement learning"
    - "convergence of value aggregation for imitation learning"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot imitation learning"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

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)