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: "autoextend extending word embeddings to embeddings for synsets and lexemes"
  Abstract: We present \textit{AutoExtend}, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.

Connected through hyperedge HE-1 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks"):
  with members:
    - "building networks for image segmentation using particle competition and cooperation"
    - "interactive image segmentation using label propagation through complex networks"
    - "complexity entropy analysis at different levels of organisation in written language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic data mining with complex networks a stylometric oriented approach"
    - "word sense disambiguation a complex network approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in narrative texts punctuation marks obey the same statistics as words"

Connected through hyperedge HE-2 (induced by source paper "what do you mean bert assessing bert as a distributional semantics model"):
  with members:
    - "distributional semantics and linguistic theory"
    - "are sixteen heads really better than one"
    - "on identifiability in transformers"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "visualizing and measuring the geometry of bert"
    - "spanbert improving pre training by representing and predicting spans"
    - "is attention interpretable"
    - "designing and interpreting probes with control tasks"

Connected through hyperedge HE-3 (induced by source paper "leveraging pre trained checkpoints for sequence generation tasks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bert rediscovers the classical nlp pipeline"
    - "sample efficient text summarization using a single pre trained transformer"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "to tune or not to tune how about the best of both worlds"
    - "discofuse a large scale dataset for discourse based sentence fusion"

Connected through hyperedge HE-4 (induced by source paper "using multi sense vector embeddings for reverse dictionaries"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "learning sentence embeddings using recursive networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-5 (induced by source paper "analyzing structures in the semantic vector space a framework for decomposing word embeddings"):
  with members:
    - "imparting interpretability to word embeddings while preserving semantic structure"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "recent trends in deep learning based natural language processing"
    - "intrinsic subspace evaluation of word embedding representations"
    - "linear algebraic structure of word senses with applications to polysemy"

Connected through hyperedge HE-6 (induced by source paper "does bert make any sense interpretable word sense disambiguation with contextualized embeddings"):
  with members:
    - "sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "roberta a robustly optimized bert pretraining approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "improving the coverage and the generalization ability of neural word sense disambiguation through hypernymy and hyponymy relationships"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "cross topic distributional semantic representations via unsupervised mappings"):
  with members:
    - "inducing and embedding senses with scaled gumbel softmax"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning rare word representations using semantic bridging"
    - "a mixture model for learning multi sense word embeddings"
    - "the umd neural machine translation systems at wmt17 bandit learning task"
    - "muse modularizing unsupervised sense embeddings"
    - "de conflated semantic representations"
    - "diachronic word embeddings reveal statistical laws of semantic change"

Connected through hyperedge HE-8 (induced by source paper "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"):
  with members:
    - "liaad at semdeep 5 challenge word in context wic"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "incorporating glosses into neural word sense disambiguation"
    - "improving the coverage and the generalization ability of neural word sense disambiguation through hypernymy and hyponymy relationships"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"

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)