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: "document classification by inversion of distributed language representations"
  Abstract: There have been many recent advances in the structure and measurement of distributed language models: those that map from words to a vector-space that is rich in information about word choice and composition. This vector-space is the distributed language representation. The goal of this note is to point out that any distributed representation can be turned into a classifier through inversion via Bayes rule. The approach is simple and modular, in that it will work with any language representation whose training can be formulated as optimizing a probability model. In our application to 2 million sentences from Yelp reviews, we also find that it performs as well as or better than complex purpose-built algorithms.

Connected through hyperedge HE-1 (induced by source paper "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "improving topic models with latent feature word representations"
    - "introducing two vietnamese datasets for evaluating semantic models of dis similarity and relatedness"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive neural network for named entity recognition in vietnamese"
    - "a fast and accurate vietnamese word segmenter"

Connected through hyperedge HE-2 (induced by source paper "training temporal word embeddings with a compass"):
  with members:
    - "tracking the evolution of words with time reflective text representations"
    - "dynamic word embeddings for evolving semantic discovery"
    - "learning distributed representations of texts and entities from knowledge base"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "semantics derived automatically from language corpora contain human like biases"
    - "tracing metaphors in time through self distance in vector spaces"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-3 (induced by source paper "dpugc learn differentially private representation for user generated contents"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "shaped shared private encoder decoder for text style adaptation"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "detecting communities under differential privacy"
    - "enriching word vectors with subword information"
    - "nonparametric spherical topic modeling with word embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "etnlp a toolkit for extraction evaluation and visualization of pre trained word embeddings"):
  with members:
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a fast and accurate vietnamese word segmenter"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "the geometry of culture analyzing the meanings of class through word embeddings"):
  with members:
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "semantics derived automatically from language corpora contain human like biases"
    - "bag of tricks for efficient text classification"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-6 (induced by source paper "personalized word representations carrying personalized semantics learned from social network posts"):
  with members:
    - "bag of tricks for efficient text classification"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "personalizing universal recurrent neural network language model with user characteristic features by social network crowdsourcing"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "predicting and understanding law making with word vectors and an ensemble model"):
  with members:
    - "gov2vec learning distributed representations of institutions and their legal text"
    - "exploring limits to prediction in complex social systems"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "predicting and understanding law making with machine learning"):
  with members:
    - "gov2vec learning distributed representations of institutions and their legal text"
    - "exploring limits to prediction in complex social systems"
    - "distributed representations of words and phrases and their compositionality"

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)