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 for teleoperation and teaming"
  Abstract: In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.

Connected through hyperedge HE-1 (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"
    - "human robot mutual adaptation in shared autonomy"
    - "uncertainty based arbitration of human machine shared control"
    - "autonomy infused teleoperation with application to bci manipulation"
    - "shared autonomy via hindsight optimization"

Connected through hyperedge HE-2 (induced by source paper "controlling assistive robots with learned latent actions"):
  with members:
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"
    - "imitating latent policies from observation"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "harmonic a multimodal dataset of assistive human robot collaboration"
    - "shared autonomy via deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "autonomy infused teleoperation with application to bci manipulation"

Connected through hyperedge HE-3 (induced by source paper "highly parallelized data driven mpc for minimal intervention shared control"):
  with members:
    - "operation and imitation under safety aware shared control"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "on infusing reachability based safety assurance within probabilistic planning frameworks for human robot vehicle interactions"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "model based control using koopman operators"

Connected through hyperedge HE-4 (induced by source paper "toward active robot assisted feeding with a general purpose mobile manipulator design evaluation and lessons learned"):
  with members:
    - "in home and remote use of robotic body surrogates by people with profound motor deficits"
    - "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "reaching in clutter with whole arm tactile sensing"

Connected through hyperedge HE-5 (induced by source paper "learning arbitration for shared autonomy by hindsight data aggregation"):
  with members:
    - "a blended human robot shared control framework to handle drift and latency"
    - "shared autonomy via deep reinforcement learning"
    - "real time perception meets reactive motion generation"
    - "human robot mutual adaptation in shared autonomy"
    - "goal set inverse optimal control and iterative re planning for predicting human reaching motions in shared workspaces"

Connected through hyperedge HE-6 (induced by source paper "towards effective human ai teams the case of collaborative packing"):
  with members:
    - "robot assisted tower construction a resource distribution task to study human robot collaboration and interaction with groups of people"
    - "bayesian policy optimization for model uncertainty"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"

Connected through hyperedge HE-7 (induced by source paper "modeling supervisor safe sets for improving collaboration in human robot teams"):
  with members:
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "safe sequential path planning of multi vehicle systems via double obstacle hamilton jacobi isaacs variational inequality"

Connected through hyperedge HE-8 (induced by source paper "mathematical models of adaptation in human robot collaboration"):
  with members:
    - "human robot mutual adaptation in shared autonomy"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"

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)