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: "simultaneous localization mapping and manipulation for unsupervised object discovery"
  Abstract: We present an unsupervised framework for simultaneous appearance-based object discovery, detection, tracking and reconstruction using RGBD cameras and a robot manipulator. The system performs dense 3D simultaneous localization and mapping concurrently with unsupervised object discovery. Putative objects that are spatially and visually coherent are manipulated by the robot to gain additional motion-cues. The robot uses appearance alone, followed by structure and motion cues, to jointly discover, verify, learn and improve models of objects. Induced motion segmentation reinforces learned models which are represented implicitly as 2D and 3D level sets to capture both shape and appearance. We compare three different approaches for appearance-based object discovery and find that a novel form of spatio-temporal super-pixels gives the highest quality candidate object models in terms of precision and recall. Live experiments with a Baxter robot demonstrate a holistic pipeline capable of automatic discovery, verification, detection, tracking and reconstruction of unknown objects.

Connected through hyperedge HE-1 (induced by source paper "building 3d object models during manipulation by reconstruction aware trajectory optimization"):
  with members:
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "active object localization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "fusing saliency maps with region proposals for unsupervised object localization"):
  with members:
    - "dilated residual networks"
    - "3d object discovery and modeling using single rgb d images containing multiple object instances"
    - "r fcn object detection via region based fully convolutional networks"
    - "salient object detection a benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"

Connected through hyperedge HE-3 (induced by source paper "temporally consistent motion segmentation from rgb d video"):
  with members:
    - "learning to segment moving objects in videos"

Connected through hyperedge HE-4 (induced by source paper "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"):
  with members:
    - "lessons from the amazon picking challenge"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "using geometry to detect grasps in 3d point clouds"
    - "imagenet large scale visual recognition challenge"

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)