human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"
  Abstract: Object pose estimation is a crucial prerequisite for robots to perform autonomous manipulation in clutter. Real-world bin-picking settings such as warehouses present additional challenges, e.g., new objects are added constantly. Most of the existing object pose estimation methods assume that 3D models of the objects is available beforehand. We present a pipeline that requires minimal human intervention and circumvents the reliance on the availability of 3D models by a fast data acquisition method and a synthetic data generation procedure. This work builds on previous work on semantic segmentation of cluttered bin-picking scenes to isolate individual objects in clutter. An additional network is trained on synthetic scenes to estimate object poses from a cropped object-centered encoding extracted from the segmentation results. The proposed method is evaluated on a synthetic validation dataset and cluttered real-world scenes.

Connected through hyperedge HE-1 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"

Connected through hyperedge HE-2 (induced by source paper "a deep learning approach to grasping the invisible"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "light weight refinenet for real time semantic segmentation"
    - "hindsight experience replay"

Question: Please tell me which class the center node 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.CV(Computer Vision and Pattern Recognition)