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: "advances in domain independent linear text segmentation"
  Abstract: This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the state-of-the-art (Reynar, 1998). Inter-sentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.

Connected through hyperedge HE-1 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "text segmentation as a supervised learning task"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-2 (induced by source paper "improving automated segmentation of radio shows with audio embeddings"):
  with members:
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "sound word2vec learning word representations grounded in sounds"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "do convolutional networks need to be deep for text classification"
    - "benchmark for complex answer retrieval"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-4 (induced by source paper "cawa an attention network for credit attribution"):
  with members:
    - "text segmentation on multilabel documents a distant supervised approach"
    - "attention based neural text segmentation"
    - "text segmentation as a supervised learning task"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "rationalizing neural predictions"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a hybrid framework for topic structure using laughter occurrences"):
  with members:
    - "an analysis of rhythmic staccato vocalization based on frequency demodulation for laughter detection in conversational meetings"
    - "a pdtb styled end to end discourse parser"

Connected through hyperedge HE-6 (induced by source paper "text segmentation using named entity recognition and co reference resolution in english and greek texts"):
  with members:
    - "a comparison of named entity recognition tools applied to biographical texts"
    - "optimal multi paragraph text segmentation by dynamic programming"

Connected through hyperedge HE-7 (induced by source paper "text segmentation based on semantic word embeddings"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-8 (induced by source paper "ontoseg a novel approach to text segmentation using ontological similarity"):
  with members:
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"
    - "linear segmentation and segment significance"

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)