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: "semantic robot programming for goal directed manipulation in cluttered scenes"
  Abstract: We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.

Connected through hyperedge HE-1 (induced by source paper "toward sim to real directional semantic grasping"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-2 (induced by source paper "spatialsense an adversarially crowdsourced benchmark for spatial relation recognition"):
  with members:
    - "explicit bias discovery in visual question answering models"
    - "linknet relational embedding for scene graph"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-3 (induced by source paper "directional semantic grasping of real world objects from simulation to reality"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (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"
    - "sum sequential scene understanding and manipulation"
    - "grasp pose detection in point clouds"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-5 (induced by source paper "semantic mapping with simultaneous object detection and localization"):
  with members:
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "multiple object detection tracking and long term dynamics learning in large 3d maps"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "meaningful maps with object oriented semantic mapping"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "factored pose estimation of articulated objects using efficient nonparametric belief propagation"):
  with members:
    - "pull message passing for nonparametric belief propagation"
    - "sum sequential scene understanding and manipulation"
    - "posecnn a convolutional neural network for 6d object pose estimation 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"

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)