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: "revisiting recurrent networks for paraphrastic sentence embeddings"
  Abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that together produce the opposite conclusion. These include training on sentence pairs rather than phrase pairs, averaging states to represent sequences, and regularizing aggressively. These improve LSTMs in both transfer learning and supervised settings. We also introduce a new recurrent architecture, the Gated Recurrent Averaging Network, that is inspired by averaging and LSTMs while outperforming them both. We analyze our learned models, finding evidence of preferences for particular parts of speech and dependency relations.

Connected through hyperedge HE-1 (induced by source paper "structural aware sentence similarity with recursive optimal transport"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "learning semantic textual similarity from conversations"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "parameter free sentence embedding via orthogonal basis"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"

Connected through hyperedge HE-2 (induced by source paper "a multi task approach for disentangling syntax and semantics in sentence representations"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "multimodal generative models for scalable weakly supervised learning"

Connected through hyperedge HE-3 (induced by source paper "a bilingual generative transformer for semantic sentence embedding"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-4 (induced by source paper "correlation coefficients and semantic textual similarity"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "encouraging paragraph embeddings to remember sentence identity improves classification"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "simple and effective paraphrastic similarity from parallel translations"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "beyond bleu training neural machine translation with semantic similarity"):
  with members:
    - "compare mt a tool for holistic comparison of language generation systems"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "regularizing neural networks by penalizing confident output distributions"

Connected through hyperedge HE-8 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"):
  with members:
    - "everything old is new again a multi view learning approach to learning using privileged information and distillation"
    - "deep learning for sentiment analysis a survey"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

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)