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: "sum sequential scene understanding and manipulation"
  Abstract: In order to perform autonomous sequential manipulation tasks, perception in cluttered scenes remains a critical challenge for robots. In this paper, we propose a probabilistic approach for robust sequential scene estimation and manipulation - Sequential Scene Understanding and Manipulation(SUM). SUM considers uncertainty due to discriminative object detection and recognition in the generative estimation of the most likely object poses maintained over time to achieve a robust estimation of the scene under heavy occlusions and unstructured environment. Our method utilizes candidates from discriminative object detector and recognizer to guide the generative process of sampling scene hypothesis, and each scene hypotheses is evaluated against the observations. Also SUM maintains beliefs of scene hypothesis over robot physical actions for better estimation and against noisy detections. We conduct extensive experiments to show that our approach is able to perform robust estimation and manipulation.

Connected through hyperedge HE-1 (induced by source paper "lit light field inference of transparency for refractive object localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "glassloc plenoptic grasp pose detection in transparent clutter"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "robust physical world attacks on deep learning models"
    - "pick and place without geometric object models"
    - "grasp pose detection in point clouds"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"

Connected through hyperedge HE-3 (induced by source paper "glassloc plenoptic grasp pose detection in transparent clutter"):
  with members:
    - "plenoptic monte carlo object localization for robot grasping under layered translucency"
    - "distinguishing refracted features using light field cameras with application to structure from motion"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "high precision grasp pose detection in dense clutter"
    - "real time grasp detection using convolutional neural networks"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-4 (induced by source paper "a sketch based system for human guided constrained object manipulation"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-5 (induced by source paper "factored pose estimation of articulated objects using efficient nonparametric belief propagation"):
  with members:
    - "pull message passing for nonparametric belief propagation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "scene grammars factor graphs and belief propagation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "loopy belief propagation for approximate inference an empirical study"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "plenoptic monte carlo object localization for robot grasping under layered translucency"):
  with members:
    - "perch perception via search for multi object recognition and localization"

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)