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 predecessor models for sample efficient imitation learning"
  Abstract: We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states. We show that this approach allows an agent to learn robust policies using only a small number of expert demonstrations and self-supervised interactions with the environment. We derive this approach from first principles and compare it empirically to a state-of-the-art imitation learning method, showing that it outperforms or matches its performance on two simulated robot manipulation tasks and demonstrate significantly higher sample efficiency by applying the algorithm on a real robot.

Connected through hyperedge HE-1 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning"):
  with members:
    - "sqil imitation learning via reinforcement learning with sparse rewards"
    - "random expert distillation imitation learning via expert policy support estimation"
    - "is the policy gradient a gradient"
    - "sqil imitation learning via regularized behavioral cloning"
    - "visual hindsight experience replay"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "support weighted adversarial imitation learning"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "exploration by random network distillation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-4 (induced by source paper "goal conditioned imitation learning"):
  with members:
    - "adaptive variance for changing sparse reward environments"
    - "self supervised learning of image embedding for continuous control"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-5 (induced by source paper "vild variational imitation learning with diverse quality demonstrations"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "an algorithmic perspective on imitation learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-6 (induced by source paper "addressing sample complexity in visual tasks using her and hallucinatory gans"):
  with members:
    - "imitating latent policies from observation"
    - "visual reinforcement learning with imagined goals"
    - "forward backward reinforcement learning"
    - "world models"
    - "few shot goal inference for visuomotor learning and planning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"

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)