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 "temporal and physical reasoning for perception based robotic manipulation"):
  Abstract: Accurate knowledge of object poses is crucial to successful robotic manipulation tasks, and yet most current approaches only work in laboratory settings. Noisy sensors and cluttered scenes interfere with accurate pose recognition, which is problematic especially when performing complex tasks involving object interactions. This is because most pose estimation algorithms focus only on estimating objects from a single frame, which means they lack continuity between frames. Further, they often do not consider resulting physical properties of the predicted scene such as intersecting objects or objects in unstable positions. In this work, we enhance the accuracy and stability of estimated poses for a whole scene by enforcing these physical constraints over time through the integration of a physics simulation. This allows us to accurately determine relationships between objects for a construction task. Scene parsing performance was evaluated on both simulated and real- world data. We apply our method to a real-world block stacking task, where the robot must build a tall tower of colored blocks.

Includes members:
  Member "semantic scene completion from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "to fall or not to fall a visual approach to physical stability prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "costar instructing collaborative robots with behavior trees and vision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "human grasp classification for reactive human to robot handovers")
  Member "approximating the volume of unions and intersections of high dimensional geometric objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the approximability of weighted model integration on dnf structures")

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)