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 "learning task oriented grasping from human activity datasets"):
  Abstract: We propose to leverage a real-world, human activity RGB datasets to teach a robot {\em Task-Oriented Grasping} (TOG). On the one hand, RGB-D datasets that contain hands and objects in interaction often lack annotations due to the manual effort in obtaining them. On the other hand, RGB datasets are often annotated with labels that do not provide enough information to infer a 6D robotic grasp pose. However, they contain examples of grasps on a variety of objects for many different tasks. Thereby, they provide a much richer source of supervision than RGB-D datasets. We propose a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increases accuracy compared to estimating these quantities independently of each other. Quantitative experiments show that training an object pose predictor with the hand pose information (and vice versa) is better than training without this information. Given the trained model, we process an RGB dataset to automatically obtain training data for a TOG model. This model takes as input an object point cloud and a task and outputs a suitable region for grasping, given the task. Qualitative experiments show that our model can successfully process a real-world dataset. Experiments with a robot demonstrate that this data allows a robot to learn task-oriented grasping on novel objects.

Includes members:
  Member "3d hand shape and pose estimation from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "learning to estimate pose and shape of hand held objects from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "learning joint reconstruction of hands and manipulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "h o unified egocentric recognition of 3d hand object poses and interactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hand priming in object localization for assistive egocentric vision")
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "learning task oriented grasping for tool manipulation from simulated self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward sim to real directional semantic grasping")
  Member "analysis of hand segmentation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of the hands in egocentric vision a survey")
  Member "global search with bernoulli alternation kernel for task oriented grasping informed by simulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

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)