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: "visual referring expression recognition what do systems actually learn"
  Abstract: We present an empirical analysis of the state-of-the-art systems for referring expression recognition -- the task of identifying the object in an image referred to by a natural language expression -- with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore the linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image $\textit{without the input referring expression}$ can achieve a precision of 71.2% in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2% in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning -- and further, how our data is constructed -- is critical as we seek to make substantive progress on grounded language tasks.

Connected through hyperedge HE-1 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "do explanations make vqa models more predictable to a human"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (induced by source paper "connecting vision and language with localized narratives"):
  with members:
    - "dense relational captioning triple stream networks for relationship based captioning"
    - "neural sequential phrase grounding seqground"
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale interactive object segmentation with human annotators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "context and attribute grounded dense captioning"

Connected through hyperedge HE-3 (induced by source paper "clevr ref diagnosing visual reasoning with referring expressions"):
  with members:
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "comprehension guided referring expressions"

Connected through hyperedge HE-4 (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"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-5 (induced by source paper "referring to objects in videos using spatio temporal identifying descriptions"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "tvqa localized compositional video question answering"
    - "actor and action video segmentation from a sentence"
    - "flow guided feature aggregation for video object detection"
    - "obj2text generating visually descriptive language from object layouts"
    - "dense captioning events in videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-6 (induced by source paper "revisiting visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "differentiable scene graphs"
    - "referring relationships"
    - "using syntax to ground referring expressions in natural images"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "open vocabulary phrase detection"
    - "grounding referring expressions in images by variational context"
    - "semantic image retrieval via active grounding of visual situations"

Connected through hyperedge HE-7 (induced by source paper "shifting the baseline single modality performance on visual navigation qa"):
  with members:
    - "prospection interpretable plans from language by predicting the future"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "annotation artifacts in natural language inference data"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-8 (induced by source paper "annotation artifacts in natural language inference data"):
  with members:
    - "natural language inference over interaction space"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "analyzing the behavior of visual question answering models"
    - "a large annotated corpus for learning natural language inference"

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)