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: "representing sentences as low rank subspaces"
  Abstract: Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences -- the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.

Connected through hyperedge HE-1 (induced by source paper "can unconditional language models recover arbitrary sentences"):
  with members:
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "measuring the intrinsic dimension of objective landscapes"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "investigating the effects of word substitution errors on sentence embeddings"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "linear transformations for cross lingual semantic textual similarity"):
  with members:
    - "a resource light method for cross lingual semantic textual similarity"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "bilingual distributed word representations from document aligned comparable data"

Connected through hyperedge HE-4 (induced by source paper "data driven methods for solving algebra word problems"):
  with members:
    - "deep contextualized word representations"
    - "opennmt open source toolkit for neural machine translation"
    - "stochastic answer networks for machine reading comprehension"
    - "a structured self attentive sentence embedding"
    - "solving general arithmetic word problems"
    - "effective approaches to attention based neural machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "correcting the common discourse bias in linear representation of sentences using conceptors"):
  with members:
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "universal sentence encoder"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "all but the top simple and effective postprocessing for word representations"
    - "towards universal paraphrastic sentence embeddings"
    - "retrofitting word vectors to semantic lexicons"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-6 (induced by source paper "identifying domain adjacent instances for semantic parsers"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"
    - "efficient estimation of word representations in vector space"

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)