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: "all but the top simple and effective postprocessing for word representations"
  Abstract: Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a {\em very simple}, and yet counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations {\em even stronger}. The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.

Connected through hyperedge HE-1 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"

Connected through hyperedge HE-2 (induced by source paper "embedding compression with isotropic iterative quantization"):
  with members:
    - "senteval an evaluation toolkit for universal sentence representations"
    - "compressing word embeddings via deep compositional code learning"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"
    - "binary paragraph vectors"
    - "compression of neural machine translation models via pruning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "a latent variable model approach to pmi based word embeddings"

Connected through hyperedge HE-3 (induced by source paper "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "learning towards minimum hyperspherical energy"
    - "frage frequency agnostic word representation"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"

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 "don t settle for average go for the max fuzzy sets and max pooled word vectors"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "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"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "representation degeneration problem in training natural language generation models"):
  with members:
    - "frage frequency agnostic word representation"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "dual supervised learning"
    - "learned in translation contextualized word vectors"
    - "regularizing and optimizing lstm language models"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "debiasing word embeddings improves multimodal machine translation"):
  with members:
    - "multimodal machine translation with embedding prediction"
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "doubly attentive decoder for multi modal neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "conceptor debiasing of word representations evaluated on weat"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "continual learning for sentence representations using conceptors"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "correcting the common discourse bias in linear representation of sentences using conceptors"

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)