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 via reward shaping"):
  Abstract: In order to learn effectively, robots must be able to extract the intangible context by which task progress and mistakes are defined. In the domain of reinforcement learning, much of this information is provided by the reward function. Hence, reward shaping is a necessary part of how we can achieve state-of-the-art results on complex, multi-step tasks. However, comparatively little work has examined how reward shaping should be done so that it captures task context, particularly in scenarios where the task is long-horizon and failure is highly consequential. Our Schedule for Positive Task (SPOT) reward trains our Efficient Visual Task (EVT) model to solve problems that require an understanding of both task context and workspace constraints of multi-step block arrangement tasks. In simulation EVT can completely clear adversarial arrangements of objects by pushing and grasping in 99% of cases vs an 82% baseline in prior work. For random arrangements EVT clears 100% of test cases at 86% action efficiency vs 61% efficiency in prior work. EVT + SPOT is also able to demonstrate context understanding and complete stacks in 74% of trials compared to a baseline of 5% with EVT alone. To our knowledge, this is the first instance of a Reinforcement Learning based algorithm successfully completing such a challenge. Code is available at this https URL .

Includes members:
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "a real time robotic grasp approach with oriented anchor box":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "reinforcement and imitation learning for diverse visuomotor skills":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "shapestacks learning vision based physical intuition for generalised object stacking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "the costar block stacking dataset learning with workspace constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")

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)