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: "speeding up context based sentence representation learning with non autoregressive convolutional decoding"
  Abstract: Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks.

Connected through hyperedge HE-1 (induced by source paper "exploiting invertible decoders for unsupervised sentence representation learning"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "dissent sentence representation learning from explicit discourse relations"

Connected through hyperedge HE-2 (induced by source paper "improving sentence representations with multi view frameworks"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "an efficient framework for learning sentence representations"
    - "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"
    - "attention is all you need"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"

Connected through hyperedge HE-3 (induced by source paper "embedding text in hyperbolic spaces"):
  with members:
    - "trimming and improving skip thought vectors"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "specialising word vectors for lexical entailment"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "hyperlex a large scale evaluation of graded lexical entailment"

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.NE(Neural and Evolutionary Computing)