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: "truncated horizon policy search combining reinforcement learning imitation learning"
  Abstract: In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one-step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance. Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning. Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal. We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.

Connected through hyperedge HE-1 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control"):
  with members:
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "robust adversarial reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-2 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on 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-3 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "neural modular control for embodied question answering"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-4 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "verifiable reinforcement learning via policy extraction"

Connected through hyperedge HE-5 (induced by source paper "guided meta policy search"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "evolved policy gradients"
    - "promp proximal meta policy search"
    - "learning to adapt meta learning for model based control"
    - "one shot visual imitation learning via meta learning"
    - "learning to learn meta critic networks for sample efficient learning"

Connected through hyperedge HE-6 (induced by source paper "information theoretic model predictive q learning"):
  with members:
    - "an online learning approach to model predictive control"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "model based active exploration"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-7 (induced by source paper "watch try learn meta learning from demonstrations and reward"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "hierarchical imitation and reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-8 (induced by source paper "imitation learning as f divergence minimization"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "sfv reinforcement learning of physical skills from videos"
    - "generative adversarial imitation from observation"
    - "sample efficient imitation learning via generative adversarial nets"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "fast policy learning through imitation and reinforcement"
    - "behavioral cloning from observation"

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)