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: "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
  Abstract: Analysis of informative contents and sentiments of social users has been attempted quite intensively in the recent past. Most of the systems are usable only for monolingual data and fails or gives poor results when used on data with code-mixing property. To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes. In this paper, we discuss the systems we prepared to collect and filter raw Twitter data. In order to reduce manual work while annotation, hybrid systems combining rule based and supervised models were developed for both language and sentiment tagging. The final corpus was annotated by a group of annotators following a few guidelines. The gold standard corpus thus obtained has impressive inter-annotator agreement obtained in terms of Kappa values. Various metrics like Code-Mixed Index (CMI), Code-Mixed Factor (CF) along with various aspects (language and emotion) also qualitatively polled the code-mixed and sentiment properties of the corpus.

Connected through hyperedge HE-1 (induced by source paper "strategies for language identification in code mixed low resource languages"):
  with members:
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "data augmentation for low resource neural machine translation"
    - "l2 regularization versus batch and weight normalization"
    - "one shot learning with memory augmented neural networks"
    - "matching networks for one shot learning"
    - "how transferable are neural networks in nlp applications"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  with members:
    - "analyzing roles of classifiers and code mixed factors for sentiment identification"
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "automatic normalization of word variations in code mixed social media text"
    - "attention is all you need"
    - "deepnorm a deep learning approach to text normalization"
    - "shallow parsing pipeline for hindi english code mixed social media text"
    - "fully character level neural machine translation without explicit segmentation"
    - "rnn approaches to text normalization a challenge"

Connected through hyperedge HE-3 (induced by source paper "language identification in code mixed data using multichannel neural networks and context capture"):
  with members:
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "design challenges and misconceptions in neural sequence labeling"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "ncrf an open source neural sequence labeling toolkit"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)