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: "a corpus for reasoning about natural language grounded in photographs"
  Abstract: We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.

Connected through hyperedge HE-1 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-2 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "deep modular co attention networks for visual question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-3 (induced by source paper "adjusting image attributes of localized regions with low level dialogue"):
  with members:
    - "a multimodal dialogue system for conversational image editing"
    - "expressing visual relationships via language"
    - "learning to globally edit images with textual description"
    - "contextual slot carryover for disparate schemas"
    - "learning to describe differences between pairs of similar images"
    - "a system for automated image editing from natural language commands"
    - "mattnet modular attention network for referring expression comprehension"
    - "sequential attention gan for interactive image editing via dialogue"

Connected through hyperedge HE-4 (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-5 (induced by source paper "12 in 1 multi task vision and language representation learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "omninet a unified architecture for multi modal multi task learning"
    - "spanbert improving pre training by representing and predicting spans"
    - "which tasks should be learned together in multi task learning"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"

Connected through hyperedge HE-6 (induced by source paper "large scale pretraining for visual dialog a simple state of the art baseline"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "vision and dialog navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"

Connected through hyperedge HE-7 (induced by source paper "meta module network for compositional visual reasoning"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "learning by abstraction the neural state machine"
    - "language conditioned graph networks for relational reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "probabilistic neural symbolic models for interpretable visual question answering"

Connected through hyperedge HE-8 (induced by source paper "lxmert learning cross modality encoder representations from transformers"):
  with members:
    - "multimodal unified attention networks for vision and language interactions"
    - "multi modality latent interaction network for visual question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "videobert a joint model for video and language representation learning"
    - "deep modular co attention networks for visual question answering"
    - "visualbert a simple and performant baseline for vision and language"
    - "cross lingual language model pretraining"

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)