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: "generalized grounding graphs a probabilistic framework for understanding grounded commands"
  Abstract: Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot’s physical surroundings. Such interpretation, known as the “grounded language problem,” is challenging both because people, especially untrained users, employ diverse vocabulary and grammar, and because in real settings robots typically have substantial uncertainty about the nature and contents of their surroundings, making it difficult to associate the constitutive language elements (principally noun phrases and spatial relations) of the command text to elements of those surroundings. Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users. Existing statistical approaches can better handle diversity, but have not to date modeled complex linguistic structure, limiting achievable accuracy. Recent hybrid approaches have addressed limitations in scaling and complexity, but have not effectively associated linguistic and perceptual features. Our framework, called Generalized Grounding Graphs (G 3 ), addresses these issues by defining a probabilistic graphical model dynamically according to the linguistic parse structure of a natural language command. This approach scales effectively, handles linguistic diversity, and enables the system to associate parts of a command with the specific objects, places, and events in the external world to which they refer. This enables robots to learn word meanings and use those learned meanings to robustly follow natural language commands produced by untrained users. We demonstrate our approach for both mobility commands (e.g. route directions like “Go down the hallway through the door”) and mobile manipulation commands (e.g. physical directives like “Pick up the pallet on the truck”) involving a variety of semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle, a forklift, and the Willow Garage PR2.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "talk2car taking control of your self driving car"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "visual referring expression recognition what do systems actually learn"

Connected through hyperedge HE-3 (induced by source paper "learning from implicit information in natural language instructions for robotic manipulations"):
  with members:
    - "semantic relational object tracking"
    - "improving grounded natural language understanding through human robot dialog"
    - "using syntax to ground referring expressions in natural images"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "neural modular control for embodied question answering"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "safe navigation with human instructions in complex scenes"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "generating realtime motion plans from complex natural language commands using dynamic grounding graphs"
    - "efficient generation of motion plans from attribute based natural language instructions using dynamic constraint mapping"
    - "proximal policy optimization algorithms"
    - "mask r cnn"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "systems of natural language facilitated human robot cooperation a review"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "understanding user instructions by utilizing open knowledge for service robots"
    - "generating machine executable plans from end user s natural language instructions"
    - "learning models for following natural language directions in unknown environments"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "language bootstrapping learning word meanings from perception action association"

Connected through hyperedge HE-6 (induced by source paper "efficient generation of motion plans from attribute based natural language instructions using dynamic constraint mapping"):
  with members:
    - "generating realtime motion plans from complex natural language commands using dynamic grounding graphs"

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.CL(Computation and Language)