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 costar block stacking dataset learning with workspace constraints"):
  Abstract: A robot can now grasp an object more effectively than ever before, but once it has the object what happens next? We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.  To address this, we introduce the JHU CoSTAR Block Stacking Dataset (BSD), where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. We discuss the ways in which this dataset provides a valuable resource for a broad range of other topics of investigation.  We find that hand-designed neural networks that work on prior datasets do not generalize to this task. Thus, to establish a baseline for this dataset, we demonstrate an automated search of neural network based models using a novel multiple-input HyperTree MetaModel, and find a final model which makes reasonable 3D pose predictions for grasping and stacking on our dataset.  The CoSTAR BSD, code, and instructions are available at https://sites.google.com/site/costardataset.

Includes members:
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "monocular object orientation estimation using riemannian regression and classification networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a disentangled embedding for monocular 3d shape retrieval and pose estimation")
  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 "a real time robotic grasp approach with oriented anchor box":
    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 "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "robustness via retrying closed loop robotic manipulation with self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action for better prediction")
  Member "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "shapestacks learning vision based physical intuition for generalised object stacking":
    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)