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: "burn in demonstrations for multi modal imitation learning"
  Abstract: Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time. Our approach involves reformulating the typical imitation learning setting to include "burn-in demonstrations" upon which policies are conditioned at test time. We demonstrate that our approach outperforms standard InfoGAIL in maximizing the mutual information between predicted and unseen style labels in road scene simulations, and we show that our method leads to policies that imitate expert autonomous driving systems over long time horizons.

Connected through hyperedge HE-1 (induced by source paper "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning to optimize neural nets"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-2 (induced by source paper "efficient black box assessment of autonomous vehicle safety"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adaptive stress testing for autonomous vehicles"
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "fuzzergym a competitive framework for fuzzing and learning"
    - "deep imitative models for flexible inference planning and control"
    - "ai benchmark running deep neural networks on android smartphones"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-3 (induced by source paper "learning a prior over intent via meta inverse reinforcement learning"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning to optimize neural nets"
    - "modular meta learning"
    - "concept learning with energy based models"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "sample efficient imitation learning via generative adversarial nets"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "parameter space noise for exploration"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "improved training of wasserstein gans"
    - "noisy networks for exploration"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-5 (induced by source paper "theory of machine networks a case study"):
  with members:
    - "machine theory of mind"
    - "the consciousness prior"
    - "infogail interpretable imitation learning from visual demonstrations"

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)