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 sentence representations"
  Abstract: We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding. We train a grounded sentence encoder that achieves good performance on COCO caption and image retrieval and subsequently show that this encoder can successfully be transferred to various NLP tasks, with improved performance over text-only models. Lastly, we analyze the contribution of grounding, and show that word embeddings learned by this system outperform non-grounded ones.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "unsupervised sentence representations as word information series revisiting tf idf"

Connected through hyperedge HE-3 (induced by source paper "on the impressive performance of randomly weighted encoders in summarization tasks"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "syntax helps elmo understand semantics is syntax still relevant in a deep neural architecture for srl"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "get to the point summarization with pointer generator networks"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-4 (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-5 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "the natural language decathlon multitask learning as question answering"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "comprehensive supersense disambiguation of english prepositions and possessives"

Connected through hyperedge HE-6 (induced by source paper "countering language drift via visual grounding"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "translating neuralese"
    - "imagination improves multimodal translation"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-7 (induced by source paper "learning semantic sentence representations from visually grounded language without lexical knowledge"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"
    - "predicting visual features from text for image and video caption retrieval"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "order embeddings and character level convolutions for multimodal alignment"
    - "lstm a search space odyssey"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (induced by source paper "no training required exploring random encoders for sentence classification"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"

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)