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 "is that a chair imagining affordances using simulations of an articulated human body"):
  Abstract: For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an imagination of object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical "sitting" interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 20 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known as a priori. In addition, we showcase that the functional pose predictions of our method on both synthetic models and real objects scanned by a depth camera align well with human judgments.

Includes members:
  Member "kpam keypoint affordances for category level robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "a deeper look at 3d shape classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "triangle net towards robustness in point cloud classification")
  Member "what can i do here leveraging deep 3d saliency and geometry for fast and scalable multiple affordance detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "egocentric affordance detection with the one shot geometry driven interaction tensor")
  Member "learning to act properly predicting and explaining affordances from images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what object should i use task driven object detection")
  Member "affordancenet an end to end deep learning approach for object affordance detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "what can i do around here deep functional scene understanding for cognitive robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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 "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pf net point fractal network for 3d point cloud completion")
  Member "3d shapenets a deep representation for volumetric shapes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)