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: "learning trajectory preferences for manipulators via iterative improvement"
  Abstract: We consider the problem of learning good trajectories for manipulation tasks. This is challenging because the criterion defining a good trajectory varies with users, tasks and environments. In this paper, we propose a co-active online learning framework for teaching robots the preferences of its users for object manipulation tasks. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this co-active preference feedback can be more easily elicited from the user than demonstrations of optimal trajectories, which are often challenging and non-intuitive to provide on high degrees of freedom manipulators. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We demonstrate the generalizability of our algorithm on a variety of grocery checkout tasks, for whom, the preferences were not only influenced by the object being manipulated but also by the surrounding environment.\footnote{For more details and a demonstration video, visit: \url{this http URL}}

Connected through hyperedge HE-1 (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"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "using causal analysis to learn specifications from task demonstrations"):
  with members:
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "counterfactual multi agent policy gradients"
    - "unsupervised learning of disentangled representations from video"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "learning from extrapolated corrections"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-4 (induced by source paper "learning preferences for manipulation tasks from online coactive feedback"):
  with members:
    - "learning policies for contextual submodular prediction"
    - "deep learning for detecting robotic grasps"
    - "continuous inverse optimal control with locally optimal examples"
    - "online structured prediction via coactive learning"
    - "learning to place new objects in a scene"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "smooth imitation learning for online sequence prediction"):
  with members:
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"

Connected through hyperedge HE-6 (induced by source paper "learning dynamic robot to human object handover from human feedback"):
  with members:
    - "learning parameterized skills"

Connected through hyperedge HE-7 (induced by source paper "robobrain large scale knowledge engine for robots"):
  with members:
    - "planit a crowdsourcing approach for learning to plan paths from large scale preference feedback"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "deep learning for detecting robotic grasps"
    - "learning object arrangements in 3d scenes using human context"
    - "contextually guided semantic labeling and search for 3d point clouds"

Connected through hyperedge HE-8 (induced by source paper "planit a crowdsourcing approach for learning to plan paths from large scale preference feedback"):
  with members:
    - "robobrain large scale knowledge engine for robots"
    - "learning object arrangements in 3d scenes using human context"
    - "learning human activities and object affordances from rgb d videos"
    - "incremental sampling based algorithms for optimal motion planning"

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)