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 grounded natural language understanding through human robot dialog"
  Abstract: Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically---continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.

Connected through hyperedge HE-1 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "vision and dialog navigation"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "a research platform for multi robot dialogue with humans"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "from recognition to cognition visual commonsense reasoning"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-3 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "on exploring undetermined relationships for visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "pointing novel objects in image captioning"
    - "context and attribute grounded dense captioning"
    - "connecting touch and vision via cross modal prediction"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-4 (induced by source paper "enabling robots to understand incomplete natural language instructions using commonsense reasoning"):
  with members:
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "a simple method for commonsense reasoning"
    - "attention based natural language grounding by navigating virtual environment"
    - "deep contextualized word representations"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "learning from implicit information in natural language instructions for robotic manipulations"):
  with members:
    - "semantic relational object tracking"
    - "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"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "just ask an interactive learning framework for vision and language navigation"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "habitat a platform for embodied ai research"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-7 (induced by source paper "neural semantic parsing with anonymization for command understanding in general purpose service robots"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "from commands to goal based dialogs a roadmap to achieve natural language interaction in robocup home"
    - "practical semantic parsing for spoken language understanding"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-8 (induced by source paper "desiderata for planning systems in general purpose service robots"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "interaction and autonomy in robocup home and building wide intelligence"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "balancing explicability and explanation in human aware planning"
    - "the strands project long term autonomy in everyday environments"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"
    - "plan explicability and predictability for robot task planning"

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)