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: "autonomy infused teleoperation with application to bci manipulation"
  Abstract: Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments.

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"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "human robot mutual adaptation in shared autonomy"
    - "uncertainty based arbitration of human machine shared control"
    - "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"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"

Connected through hyperedge HE-3 (induced by source paper "variable scaling rate control for collision free teleoperation of an unmanned aerial vehicle"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization"

Connected through hyperedge HE-4 (induced by source paper "entropy based motion intention identification for brain computer interface"):
  with members:
    - "brain computer interface meets ros a robotic approach to mentally drive telepresence robots"

Connected through hyperedge HE-5 (induced by source paper "one shot imitation from observing humans via domain adaptive meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "virtual to real reinforcement learning for autonomous driving"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to generalize meta learning for domain generalization"
    - "satellite image based localization via learned embeddings"
    - "learning robot activities from first person human videos using convolutional future regression"

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)