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 "transferable task execution from pixels through deep planning domain learning"):
  Abstract: While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic tasks due to the challenges of grounding these symbols from sensor data in a partially-observable world. We propose Deep Planning Domain Learning (DPDL), an approach that combines the strengths of both methods to learn a hierarchical model. DPDL learns a high-level model which predicts values for a large set of logical predicates consisting of the current symbolic world state, and separately learns a low-level policy which translates symbolic operators into executable actions on the robot. This allows us to perform complex, multi-step tasks even when the robot has not been explicitly trained on them. We show our method on manipulation tasks in a photorealistic kitchen scenario.

Includes members:
  Member "representing robot task plans as robust logical dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "human grasp classification for reactive human to robot handovers")
  Member "prospection interpretable plans from language by predicting the future":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")
  Member "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "neural task graphs generalizing to unseen tasks from a single video demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to generalize across long horizon tasks from human demonstrations")
  Member "stripstream integrating symbolic planners and blackbox samplers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online replanning in belief space for partially observable task and motion problems")
  Member "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "integrating acting planning and learning in hierarchical operational models")
  Member "one shot hierarchical imitation learning of compound visuomotor tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "squirl robust and efficient learning from video demonstration of long horizon robotic manipulation tasks")
  Member "visual robot task planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "model based reinforcement learning for atari")

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)