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: "shared autonomy via hindsight optimization"
  Abstract: In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user's intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user's goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user's goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly.

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"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"

Connected through hyperedge HE-2 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "human robot mutual adaptation in shared autonomy"
    - "uncertainty based arbitration of human machine shared control"
    - "autonomy infused teleoperation with application to bci manipulation"

Connected through hyperedge HE-3 (induced by source paper "learn task first or learn human partner first deep reinforcement learning of human robot cooperation in asymmetric hierarchical dynamic task"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "human robot mutual adaptation in shared autonomy"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "an intent based task aware shared control framework for intuitive object telemanipulation"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "grasping for a purpose using task goals for efficient manipulation planning"

Connected through hyperedge HE-5 (induced by source paper "learning reward functions by integrating human demonstrations and preferences"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "simplifying reward design through divide and conquer"
    - "reward learning from human preferences and demonstrations in atari"
    - "batch active preference based learning of reward functions"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "on the utility of learning about humans for human ai coordination"):
  with members:
    - "on the utility of model learning in hri"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "learning existing social conventions via observationally augmented self play"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-7 (induced by source paper "on the utility of model learning in hri"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "proximal policy optimization algorithms"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "unsupervised learning for physical interaction through video prediction"
    - "trust region policy optimization"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "synthesis of provably correct autonomy protocols for shared control"):
  with members:
    - "omega regular objectives in model free reinforcement learning"
    - "a storm is coming a modern probabilistic model checker"
    - "synthesis of shared control protocols with provable safety and performance guarantees"

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.RO(Robotics)