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: "automatic dialect detection in arabic broadcast speech"
  Abstract: We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework. We studied both generative and discriminate classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further report results using the proposed method to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 52%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. We also release the train and test data as standard corpus for dialect identification.

Connected through hyperedge HE-1 (induced by source paper "moroco the moldavian and romanian dialectal corpus"):
  with members:
    - "improving the results of string kernels in sentiment analysis and arabic dialect identification by adapting them to your test set"
    - "automatic identification of closely related indian languages resources and experiments"
    - "automated essay scoring with string kernels and word embeddings"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "can string kernels pass the test of time in native language identification"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "language modeling with gated convolutional networks"
    - "a character level convolutional neural network for distinguishing similar languages and dialects"

Connected through hyperedge HE-2 (induced by source paper "arabic natural language processing an overview"):
  with members:
    - "madari a web interface for joint arabic morphological annotation and spelling correction"
    - "asda analyseur syntaxique du dialecte alg e rien dans un but d analyse s e mantique"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "sequence to sequence neural network models for transliteration"
    - "a large scale corpus of gulf arabic"
    - "bag of tricks for efficient text classification"
    - "character aware neural language models"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "detecting deception in political debates using acoustic and textual features"):
  with members:
    - "fact checking meets fauxtography verifying claims about images"
    - "all in one multi task learning for rumour verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a deep learning approach for multimodal deception detection"
    - "fully automated fact checking using external sources"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"):
  with members:
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "proppy a system to unmask propaganda in online news"
    - "predicting factuality of reporting and bias of news media sources"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "multi view models for political ideology detection of news articles"
    - "on measuring bias in online information"

Connected through hyperedge HE-5 (induced by source paper "multimodal fusion with deep neural networks for audio video emotion recognition"):
  with members:
    - "emotion recognition using fusion of audio and video features"
    - "memory integrity of cnns for cross dataset facial expression recognition"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-6 (induced by source paper "automatic language identification in texts a survey"):
  with members:
    - "n gram and neural language models for discriminating similar languages"
    - "including dialects and language varieties in author profiling"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "labeling of query words using conditional random field"
    - "discriminating similar languages evaluations and explorations"
    - "language detection for short text messages in social media"
    - "recurrent neural network for language detection on twitter code switching corpus"

Connected through hyperedge HE-7 (induced by source paper "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"):
  with members:
    - "convolutional neural networks and language embeddings for end to end dialect recognition"
    - "automatic language identification in texts a survey"
    - "automated essay scoring with string kernels and word embeddings"
    - "can string kernels pass the test of time in native language identification"
    - "speech recognition challenge in the wild arabic mgb 3"

Connected through hyperedge HE-8 (induced by source paper "classifier ensembles for dialect and language variety identification"):
  with members:
    - "automatic language identification in texts a survey"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "discriminating between indo aryan languages using svm ensembles"
    - "speech recognition challenge in the wild arabic mgb 3"

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)