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: "understanding visual ads by aligning symbols and objects using co attention"
  Abstract: We tackle the problem of understanding visual ads where given an ad image, our goal is to rank appropriate human generated statements describing the purpose of the ad. This problem is generally addressed by jointly embedding images and candidate statements to establish correspondence. Decoding a visual ad requires inference of both semantic and symbolic nuances referenced in an image and prior methods may fail to capture such associations especially with weakly annotated symbols. In order to create better embeddings, we leverage an attention mechanism to associate image proposals with symbols and thus effectively aggregate information from aligned multimodal representations. We propose a multihop co-attention mechanism that iteratively refines the attention map to ensure accurate attention estimation. Our attention based embedding model is learned end-to-end guided by a max-margin loss function. We show that our model outperforms other baselines on the benchmark Ad dataset and also show qualitative results to highlight the advantages of using multihop co-attention.

Connected through hyperedge HE-1 (induced by source paper "align2ground weakly supervised phrase grounding guided by image caption alignment"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "multi level multimodal common semantic space for image phrase grounding"
    - "using syntax to ground referring expressions in natural images"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "learning two branch neural networks for image text matching tasks"
    - "natural language does not emerge naturally in multi agent dialog"

Connected through hyperedge HE-2 (induced by source paper "beyond visual semantics exploring the role of scene text in image understanding"):
  with members:
    - "towards vqa models that can read"
    - "exploring hate speech detection in multimodal publications"
    - "relation aware graph attention network for visual question answering"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "fots fast oriented text spotting with a unified network"
    - "textually enriched neural module networks for visual question answering"
    - "don t only feel read using scene text to understand advertisements"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "percival making in browser perceptual ad blocking practical with deep learning"):
  with members:
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "adversarial logit pairing"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial attacks on neural network policies"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "the future of ad blocking an analytical framework and new techniques"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "advise symbolism and external knowledge for decoding advertisements"

Connected through hyperedge HE-4 (induced by source paper "integrating text and image determining multimodal document intent in instagram posts"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep contextualized word representations"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "knowledge rich image gist understanding beyond literal meaning"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "viraliency pooling local virality"

Connected through hyperedge HE-5 (induced by source paper "semantically aware attentive neural embeddings for image based visual localization"):
  with members:
    - "deep audio visual speech recognition"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "attention based pyramid aggregation network for visual place recognition"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "night to day image translation for retrieval based localization"
    - "long term visual localization using semantically segmented images"
    - "cbam convolutional block attention module"
    - "sampling matters in deep embedding learning"

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.CV(Computer Vision and Pattern Recognition)