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: "variations of the similarity function of textrank for automated summarization"
  Abstract: This article presents new alternatives to the similarity function for the TextRank algorithm for automatic summarization of texts. We describe the generalities of the algorithm and the different functions we propose. Some of these variants achieve a significative improvement using the same metrics and dataset as the original publication.

Connected through hyperedge HE-1 (induced by source paper "detecting new word meanings a comparison of word embedding models in spanish"):
  with members:
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "sense2vec a fast and accurate method for word sense disambiguation in neural word embeddings"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "bigpatent a large scale dataset for abstractive and coherent summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "detecting un important content for single document news summarization"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "sentence centrality revisited for unsupervised summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "strass a light and effective method for extractive summarization based on sentence embeddings"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised domain detection"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "attention is all you need"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "very deep convolutional networks for text classification"
    - "rationale augmented convolutional neural networks for text classification"
    - "domain separation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "wikihow a large scale text summarization dataset"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-7 (induced by source paper "neural network architecture for credibility assessment of textual claims"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "estimating continuous distributions in bayesian classifiers"

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)