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: "efficient large scale multi modal classification"
  Abstract: While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate that the inclusion of continuous information improves performance over text-only on a range of multi-modal classification tasks, even with simple fusion methods. In addition, we experiment with discretizing the continuous features in order to speed up and simplify the fusion process even further. Our results show that fusion with discretized features outperforms text-only classification, at a fraction of the computational cost of full multi-modal fusion, with the additional benefit of improved interpretability.

Connected through hyperedge HE-1 (induced by source paper "what makes training multi modal classification networks hard"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hypothesis only baselines in natural language inference"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-2 (induced by source paper "semantic aware scene recognition"):
  with members:
    - "auto reid searching for a part aware convnet for person re identification"
    - "rethinking imagenet pre training"
    - "deep frank wolfe for neural network optimization"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "in pixels we trust from pixel labeling to object localization and scene categorization"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "supervised multimodal bitransformers for classifying images and text"):
  with members:
    - "what makes training multi modal networks hard"
    - "lxmert learning cross modality encoder representations from transformers"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "annotation artifacts in natural language inference data"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "picture what you read"):
  with members:
    - "aiding intra text representations with visual context for multimodal named entity recognition"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "image and encoded text fusion for multi modal classification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "adversarial nets with perceptual losses for text to image synthesis"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-5 (induced by source paper "dual modality seq2seq network for audio visual event localization"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "audio visual event localization in unconstrained videos"
    - "the sound of pixels"
    - "objects that sound"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "deep multimodal representation learning from temporal data"
    - "look listen and learn"

Connected through hyperedge HE-6 (induced by source paper "one embedding to do them all"):
  with members:
    - "personalizing similar product recommendations in fashion e commerce"
    - "visually aware fashion recommendation and design with generative image models"
    - "specializing joint representations for the task of product recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "line large scale information network embedding"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-7 (induced by source paper "aiding intra text representations with visual context for multimodal named entity recognition"):
  with members:
    - "revisiting cross modal retrieval"
    - "learning word vectors for 157 languages"
    - "multimodal named entity recognition for short social media posts"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a multi task approach for named entity recognition in social media data"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "dual attention networks for multimodal reasoning and matching"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-8 (induced by source paper "discriminative regression machine a classifier for high dimensional data or imbalanced data"):
  with members:
    - "dynamic curriculum learning for imbalanced data classification"
    - "smote synthetic minority over sampling technique"

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)