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 "the state of service robots current bottlenecks in object perception and manipulation"):
  Abstract: Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to adapt to new environments through time and interact with non-expert human users safely. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object. While there are many successes, the robot should be painstakingly coded in advance to perform a set of predefined tasks. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In this paper, we review advances in service robots from object perception to complex object manipulation and shed a light on the current challenges and bottlenecks.

Includes members:
  Member "learning to singulate objects using a push proposal network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition":
    also connected through other hyperedges:
      (none)
  Member "accelerating reinforcement learning for reaching using continuous curriculum learning":
    also connected through other hyperedges:
      (none)
  Member "4d spatio temporal convnets minkowski convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the whole is greater than the sum of its nonrigid parts")
  Member "learning better generative models for dexterous single view grasping of novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep dexterous grasping of novel objects from a single view")
  Member "6 dof grasping for target driven object manipulation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "the sound of motions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "co separating sounds of visual objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds")

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)