human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mit qcri arabic dialect identification system for the 2017 multi genre broadcast challenge"):
  Abstract: In order to successfully annotate the Arabic speech con- tent found in open-domain media broadcasts, it is essential to be able to process a diverse set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3) there were two possible tasks: Arabic speech recognition, and Arabic Dialect Identification (ADI). In this paper, we describe our efforts to create an ADI system for the MGB-3 challenge, with the goal of distinguishing amongst four major Arabic dialects, as well as Modern Standard Arabic. Our research fo- cused on dialect variability and domain mismatches between the training and test domain. In order to achieve a robust ADI system, we explored both Siamese neural network models to learn similarity and dissimilarities among Arabic dialects, as well as i-vector post-processing to adapt domain mismatches. Both Acoustic and linguistic features were used for the final MGB-3 submissions, with the best primary system achieving 75% accuracy on the official 10hr test set.

Includes members:
  Member "recursive whitening transformation for speaker recognition on language mismatched condition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "delving into voxceleb environment invariant speaker recognition")
  Member "speech recognition challenge in the wild arabic mgb 3":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moroco the moldavian and romanian dialectal corpus")
  Member "autoencoder based domain adaptation for speaker recognition under insufficient channel information":
    also connected through other hyperedges:
      (none)
  Member "ku ispl speaker recognition systems under language mismatch condition for nist 2016 speaker recognition evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional neural networks and language embeddings for end to end dialect recognition")
  Member "the mgb 2 challenge arabic multi dialect broadcast media recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition")
  Member "a unified deep neural network for speaker and language recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition")
  Member "automatic dialect detection in arabic broadcast speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "moroco the moldavian and romanian dialectal corpus")

Question: Please tell me which class the source paper 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)