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 "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  Abstract: In order to effectively learn multi-step tasks, robots must be able to understand the context by which task progress is defined. In reinforcement learning, much of this information is provided to the learner by the reward function. However, comparatively little work has examined how the reward function captures - or fails to capture - task context in robotics, particularly in long-horizon tasks where failure is highly consequential. To address this issue, we describe the Schedule for Positive Task (SPOT) Reward and the SPOT-Q reinforcement learning algorithm, which efficiently learn multi-step block manipulation tasks in both simulation and real-world environments. SPOT-Q is remarkably effective compared to past benchmarks. It successfully completes simulated trials of a variety of tasks including stacking cubes (98%), clearing toys by pushing and grasping arranged in random (100%) and adversarial (95%) patterns, and creating rows of cubes (93%). Furthermore, we demonstrate direct sim to real transfer. By directly loading the simulation-trained model on the real robot, we are able to create real stacks in 90% of trials and rows in 80% of trials with no additional real-world fine-tuning. Our system is also quite efficient - models train within 1-10k actions, depending on the task. As a result, our algorithm makes learning complex, multi-step tasks both efficient and practical for real world manipulation tasks. Code is available at https://github.com/jhu-lcsr/good_robot .

Includes members:
  Member "6 dof grasping for target driven object manipulation in clutter":
    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 "form2fit learning shape priors for generalizable assembly from disassembly":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards intelligent pick and place assembly of individualized products using reinforcement learning")
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "a review of robot learning for manipulation challenges representations and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data")
  Member "6 dof graspnet variational grasp generation for object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")

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)