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 "deep gated multi modal learning in hand object pose changes estimation using tactile and image data"):
  Abstract: For in-hand manipulation, estimation of the object pose inside the hand is one of the important functions to manipulate objects to the target pose. Since in-hand manipulation tends to cause occlusion by the hand or the object itself, image information only is not sufficient for in-hand object pose estimation. Multiple modalities can be used in this case, the advantage is that other modalities can compensate for occlusion, noise, and sensor malfunctions. Even though deciding the utilization rate of a modality (referred to as reliability value) corresponding to the situations is important, the manual design of such models is difficult, especially for various situations. In this paper, we propose deep gated multi-modal learning, which self-determines the reliability value of each modality through end-to-end deep learning. For the experiments, an RGB camera and a GelSight tactile sensor were attached to the parallel gripper of the Sawyer robot, and the object pose changes were estimated during grasping. A total of 15 objects were used in the experiments. In the proposed model, the reliability values of the modalities were determined according to the noise level and failure of each modality, and it was confirmed that the pose change was estimated even for unknown objects.

Includes members:
  Member "densefusion 6d object pose estimation by iterative dense fusion":
    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 "bop benchmark for 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "never give up learning directed exploration strategies")
  Member "more than a feeling learning to grasp and regrasp using vision and touch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "segmentation driven 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "robust deep multi modal learning based on gated information fusion network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trained model fusion for object detection using gating network")
  Member "interactively picking real world objects with unconstrained spoken language instructions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")

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)