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: "stay on the path instruction fidelity in vision and language navigation"
  Abstract: Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation(VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al.,2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.

Connected through hyperedge HE-1 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation"):
  with members:
    - "multi modal discriminative model for vision and language navigation"
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "domain agnostic learning with disentangled representations"
    - "valan vision and language agent navigation"
    - "transferable representation learning in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"

Connected through hyperedge HE-2 (induced by source paper "perceive transform and act multi modal attention networks for vision and language navigation"):
  with members:
    - "embodied vision and language navigation with dynamic convolutional filters"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "embodied visual recognition"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "situational fusion of visual representation for visual navigation"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-3 (induced by source paper "learning to map natural language instructions to physical quadcopter control using simulated flight"):
  with members:
    - "executing instructions in situated collaborative interactions"
    - "chasing ghosts instruction following as bayesian state tracking"
    - "universal planning networks"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "vision and dialog navigation"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "a research platform for multi robot dialogue with humans"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "improving grounded natural language understanding through human robot dialog"
    - "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-5 (induced by source paper "counterfactual vision and language navigation via adversarial path sampling"):
  with members:
    - "adversarial active exploration for inverse dynamics model learning"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "transferable representation learning in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "counterfactual visual explanations"
    - "learning to navigate unseen environments back translation with environmental dropout"

Connected through hyperedge HE-6 (induced by source paper "cross lingual vision language navigation"):
  with members:
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "self monitoring navigation agent via auxiliary 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"
    - "speaker follower models for vision and language navigation"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "valan vision and language agent navigation"):
  with members:
    - "vision and dialog navigation"
    - "seed rl scalable and efficient deep rl with accelerated central inference"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "transferable representation learning in vision and language navigation"
    - "dopamine a research framework for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "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-8 (induced by source paper "effective and general evaluation for instruction conditioned navigation using dynamic time warping"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "the streetlearn environment and dataset"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "on evaluation of embodied navigation agents"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "mapping navigation instructions to continuous control actions with position visitation prediction"
    - "matterport3d learning from rgb d data in indoor environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"

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.AI(Artificial Intelligence)