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: "unsupervised textual grounding linking words to image concepts"
  Abstract: Textual grounding, i.e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction. Existing techniques benefit from recent progress in deep learning and generally formulate the task as a supervised learning problem, selecting a bounding box from a set of possible options. To train these deep net based approaches, access to a large-scale datasets is required, however, constructing such a dataset is time-consuming and expensive. Therefore, we develop a completely unsupervised mechanism for textual grounding using hypothesis testing as a mechanism to link words to detected image concepts. We demonstrate our approach on the ReferIt Game dataset and the Flickr30k data, outperforming baselines by 7.98% and 6.96% respectively.

Connected through hyperedge HE-1 (induced by source paper "modularized textual grounding for counterfactual resilience"):
  with members:
    - "weakly supervised attention learning for textual phrases grounding"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "w talc weakly supervised temporal activity localization and classification"
    - "mattnet modular attention network for referring expression comprehension"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "exploring the limits of weakly supervised pretraining"
    - "explainable neural computation via stack neural module networks"

Connected through hyperedge HE-2 (induced by source paper "phrase localization without paired training examples"):
  with members:
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "conditional image text embedding networks"
    - "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-3 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "an unsupervised autoregressive model for speech representation learning"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-4 (induced by source paper "vital a visual interpretation on text with adversarial learning for image labeling"):
  with members:
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "conditional image text embedding networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "multichannel variable size convolution for sentence classification"
    - "learning what and where to draw"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-5 (induced by source paper "asu at textgraphs 2019 shared task explanation regeneration using language models and iterative re ranking"):
  with members:
    - "careful selection of knowledge to solve open book question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"

Connected through hyperedge HE-6 (induced by source paper "multi level multimodal common semantic space for image phrase grounding"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "grounding visual explanations"
    - "learning unsupervised visual grounding through semantic self supervision"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "two can play this game visual dialog with discriminative question generation and answering"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "creativity generating diverse questions using variational autoencoders"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "high order attention models for visual question answering"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised attention learning for textual phrases grounding"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "query guided regression network with context policy for phrase grounding"
    - "range loss for deep face recognition with long tail"
    - "deep feature consistent variational autoencoder"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "multi scale orderless pooling of deep convolutional activation features"

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)