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: "learning visually grounded semantics from contrastive adversarial samples"
  Abstract: We study the problem of grounding distributional representations of texts on the visual domain, namely visual-semantic embeddings (VSE for short). Begin with an insightful adversarial attack on VSE embeddings, we show the limitation of current frameworks and image-text datasets (e.g., MS-COCO) both quantitatively and qualitatively. The large gap between the number of possible constitutions of real-world semantics and the size of parallel data, to a large extent, restricts the model to establish the link between textual semantics and visual concepts. We alleviate this problem by augmenting the MS-COCO image captioning datasets with textual contrastive adversarial samples. These samples are synthesized using linguistic rules and the WordNet knowledge base. The construction procedure is both syntax- and semantics-aware. The samples enforce the model to ground learned embeddings to concrete concepts within the image. This simple but powerful technique brings a noticeable improvement over the baselines on a diverse set of downstream tasks, in addition to defending known-type adversarial attacks. We release the codes at this https URL.

Connected through hyperedge HE-1 (induced by source paper "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"):
  with members:
    - "word learning and the acquisition of syntactic semantic overhypotheses"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "object level visual reasoning in videos"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "univse robust visual semantic embeddings via structured semantic representations"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "image generation from scene graphs"
    - "foil it find one mismatch between image and language caption"
    - "vse improving visual semantic embeddings with hard negatives"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "a structured self attentive sentence embedding"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-3 (induced by source paper "hal improved text image matching by mitigating visual semantic hubs"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "a strong and robust baseline for text image matching"
    - "stacked cross attention for image text matching"
    - "improving generalization via scalable neighborhood component analysis"
    - "engaging image captioning via personality"
    - "sampling matters in deep embedding learning"
    - "foil it find one mismatch between image and language caption"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-4 (induced by source paper "visually grounded neural syntax acquisition"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "cooperative learning of disjoint syntax and semantics"
    - "on tree based neural sentence modeling"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi head attention with diversity for learning grounded multilingual multimodal representations"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "imagination improves multimodal translation"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "a strong and robust baseline for text image matching"):
  with members:
    - "good news everyone context driven entity aware captioning for news images"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "stacked cross attention for image text matching"
    - "foil it find one mismatch between image and language caption"
    - "learning two branch neural networks for image text matching tasks"
    - "vse improving visual semantic embeddings with hard negatives"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-7 (induced by source paper "on tree based neural sentence modeling"):
  with members:
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "deep rnns encode soft hierarchical syntax"
    - "structured attention networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "modeling source syntax for neural machine translation"

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)