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: "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
  Abstract: Developing robot perception systems for recognizing objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. This paper presents the EasyLabel tool for easily acquiring high quality ground truth annotation of objects at the pixel-level in densely cluttered scenes. In a semi-automatic process, complex scenes are incrementally built and EasyLabel exploits depth change to extract precise object masks at each step. We use this tool to generate the Object Cluttered Indoor Dataset (OCID) that captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. OCID is used to perform a systematic comparison of existing object segmentation methods. The baseline comparison supports the need for pixel- and object-wise annotation to progress robot vision towards realistic applications. This insight reveals the usefulness of EasyLabel and OCID to better understand the challenges that robots face in the real-world. #R##N#Copyright 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Connected through hyperedge HE-1 (induced by source paper "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "towards segmenting everything that moves"
    - "large scale interactive object segmentation with human annotators"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "group normalization"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-2 (induced by source paper "pose estimation for texture less shiny objects in a single rgb image using synthetic training data"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "falling things a synthetic dataset for 3d object detection and pose estimation"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "real time seamless single shot 6d object pose prediction"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"

Connected through hyperedge HE-3 (induced by source paper "recurrent convolutional fusion for rgb d object recognition"):
  with members:
    - "mask r cnn"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "a deep representation for depth images from synthetic data"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "deep residual learning for image recognition"
    - "multimodal deep learning for robust rgb d object recognition"

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.RO(Robotics)