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: "bridge correlational neural networks for multilingual multimodal representation learning"
  Abstract: Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.

Connected through hyperedge HE-1 (induced by source paper "mule multimodal universal language embedding"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "vico word embeddings from visual co occurrences"
    - "language features matter effective language representations for vision language tasks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unpaired image captioning by language pivoting"
    - "multi task learning of hierarchical vision language representation"

Connected through hyperedge HE-2 (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"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "aligning multilingual word embeddings for cross modal retrieval task"):
  with members:
    - "neural baby talk"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "learning two branch neural networks for image text matching tasks"
    - "multilingual multi modal embeddings for natural language processing"
    - "image pivoting for learning multilingual multimodal representations"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "word translation without parallel data"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-4 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "deep generalized canonical correlation analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"

Connected through hyperedge HE-5 (induced by source paper "lessons learned in multilingual grounded language learning"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning language representations for typology prediction"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-7 (induced by source paper "neural compatibility modeling with attentive knowledge distillation"):
  with members:
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "an attention based collaboration framework for multi view network representation learning"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-8 (induced by source paper "multi lingual common semantic space construction via cluster consistent word embedding"):
  with members:
    - "learning joint multilingual sentence representations with neural machine translation"
    - "word translation without parallel data"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "massively multilingual word embeddings"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "ultradense word embeddings by orthogonal transformation"
    - "neural architectures for named entity recognition"
    - "many languages one parser"

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)