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 "towards robust product packing with a minimalistic end effector"):
  Abstract: Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing finger. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline, which incrementally introduce reasoning about object poses and corrective manipulation actions.

Includes members:
  Member "robust 6d object pose estimation with stochastic congruent sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "fast object learning and dual arm coordination for cluttered stowing picking and packing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint learning of instance and semantic segmentation for robotic pick and place with heavy occlusions in clutter")
  Member "a self supervised learning system for object detection using physics simulation and multi view pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "template based unseen instance detection")
  Member "cartman the low cost cartesian manipulator that won the amazon robotics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointins point based instance segmentation")
  Member "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "analysis and observations from the first amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust 6d object pose estimation by learning rgb d features")

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)