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: "variational inverse control with events a general framework for data driven reward definition"
  Abstract: The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning attempt to overcome this challenge, but require expert demonstrations, which can be difficult or expensive to obtain in practice. We propose variational inverse control with events (VICE), which generalizes inverse reinforcement learning methods to cases where full demonstrations are not needed, such as when only samples of desired goal states are available. Our method is grounded in an alternative perspective on control and reinforcement learning, where an agent's goal is to maximize the probability that one or more events will happen at some point in the future, rather than maximizing cumulative rewards. We demonstrate the effectiveness of our methods on continuous control tasks, with a focus on high-dimensional observations like images where rewards are hard or even impossible to specify.

Connected through hyperedge HE-1 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "trajectory deformations from physical human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

Connected through hyperedge HE-3 (induced by source paper "end to end robotic reinforcement learning without reward engineering"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"

Connected through hyperedge HE-4 (induced by source paper "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "deep imitative models for flexible inference planning and control"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "hierarchical imitation and reinforcement learning"
    - "maximum a posteriori policy optimisation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-5 (induced by source paper "avid learning multi stage tasks via pixel level translation of human videos"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "imitating latent policies from observation"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-6 (induced by source paper "doubly robust off policy actor critic algorithms for reinforcement learning"):
  with members:
    - "when to trust your model model based policy optimization"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "more robust doubly robust off policy evaluation"
    - "reward estimation for variance reduction in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "reinforcement learning with a corrupted reward channel"

Connected through hyperedge HE-7 (induced by source paper "an extensible interactive interface for agent design"):
  with members:
    - "visual reinforcement learning with imagined goals"
    - "reward learning from human preferences and demonstrations in atari"
    - "learning to understand goal specifications by modelling reward"
    - "shared autonomy via deep reinforcement learning"
    - "guiding policies with language via meta learning"
    - "hindsight experience replay"
    - "grounded language learning in a simulated 3d world"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "solar deep structured latent representations for model based reinforcement learning"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

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)