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 "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}}

Includes members:
  Member "deep learning for detecting robotic grasps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "continuous inverse optimal control with locally optimal examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")
  Member "online structured prediction via coactive learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "human preference based learning for high dimensional optimization of exoskeleton walking gaits")
  Member "learning object arrangements in 3d scenes using human context":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "learning to place new objects in a scene":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "incremental sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian local sampling based planning")

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)