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: "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"
  Abstract: This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detection and global point cloud registration techniques. The best-scored pose per object by using these techniques may not be accurate due to overlaps and occlusions. Nevertheless, experimental indications provided in this work show that object poses with lower ranks may be closer to the real poses than ones with high ranks according to registration techniques. This motivates a global optimization process for improving these poses by taking into account scene-level physical interactions between objects. It also implies that the Cartesian product of candidate poses for interacting objects must be searched so as to identify the best scene-level hypothesis. To perform the search efficiently, the candidate poses for each object are clustered so as to reduce their number but still keep a sufficient diversity. Then, searching over the combinations of candidate object poses is performed through a Monte Carlo Tree Search (MCTS) process that uses the similarity between the observed depth image of the scene and a rendering of the scene given the hypothesized pose as a score that guides the search procedure. MCTS handles in a principled way the tradeoff between fine-tuning the most promising poses and exploring new ones, by using the Upper Confidence Bound (UCB) technique. Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.

Connected through hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "learning joint reconstruction of hands and manipulated objects"
    - "manipulation by feel touch based control with deep predictive models"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"

Connected through hyperedge HE-2 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "graspa 1 0 graspa is a robot arm grasping performance benchmark"
    - "dpod 6d pose object detector and refiner"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "towards robust product packing with a minimalistic end effector"):
  with members:
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "analysis and observations from the first amazon picking challenge"
    - "global hypothesis generation for 6d object pose estimation"

Connected through hyperedge HE-5 (induced by source paper "physics based scene level reasoning for object pose estimation in clutter"):
  with members:
    - "fusion volumetric object level slam"
    - "bop benchmark for 6d object pose estimation"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "6d pose estimation using an improved method based on point pair features"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "genesis rt generating synthetic images for training secondary real world tasks"

Connected through hyperedge HE-6 (induced by source paper "learning object localization and 6d pose estimation from simulation and weakly labeled real images"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "deep visual domain adaptation a survey"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "multi adversarial domain adaptation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "6d pose estimation using an improved method based on point pair features"

Connected through hyperedge HE-7 (induced by source paper "a summary of the 4th international workshop on recovering 6d object pose"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "3d coded 3d correspondences by deep deformation"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "fully convolutional point networks for large scale point clouds"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-8 (induced by source paper "making deep heatmaps robust to partial occlusions for 3d object pose estimation"):
  with members:
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

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)