human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "controlling assistive robots with learned latent actions"):
  Abstract: Assistive robots enable users with disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms useful also makes them challenging to control: the robot has more degrees-of-freedom (DoFs) than the human can directly coordinate with a handheld joystick. Our insight is that we can make assistive robots easier for humans to control by learning latent actions. Latent actions provide a low-DoF embedding of high-DoF robot behavior: for example, one latent dimension might guide the robot arm along a pouring motion. Because these latent actions are low-dimensional, they can be controlled by the human end-user to fluidly teleoperate the robot. In this paper, we design a teleoperation algorithm for assistive robots that learns intuitive latent dimensions from task demonstrations. We formulate the controllability, consistency, and scaling properties that intuitive latent actions should have, and evaluate how different low-dimensional embeddings capture these properties. Finally, we conduct two user studies on a robotic arm to compare our latent action approach to state-of-the-art shared autonomy baselines and direct end-effector teleoperation. Participants completed the assistive feeding and cooking tasks more quickly and efficiently when leveraging our latent actions, and also reported that latent actions made the task easier to perform. The video accompanying this paper can be found at: this https URL.

Includes members:
  Member "learning latent plans from play":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "learning action representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "imitating latent policies from observation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning predictive models from observation and interaction")
  Member "learning models for shared control of human machine systems with unknown dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active learning of dynamics for data driven control using koopman operators")
  Member "harmonic a multimodal dataset of assistive human robot collaboration":
    also connected through other hyperedges:
      (none)
  Member "shared autonomy via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real world human robot collaborative reinforcement learning")
  Member "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "shared autonomy via hindsight optimization for teleoperation and teaming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling")

Question: Please tell me which class the source paper 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)