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: "aishell 2 transforming mandarin asr research into industrial scale"
  Abstract: AISHELL-1 is by far the largest open-source speech corpus available for Mandarin speech recognition research. It was released with a baseline system containing solid training and testing pipelines for Mandarin ASR. In AISHELL-2, 1000 hours of clean read-speech data from iOS is published, which is free for academic usage. On top of AISHELL-2 corpus, an improved recipe is developed and released, containing key components for industrial applications, such as Chinese word segmentation, flexible vocabulary expension and phone set transformation etc. Pipelines support various state-of-the-art techniques, such as time-delayed neural networks and Lattic-Free MMI objective funciton. In addition, we also release dev and test data from other channels(Android and Mic). For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR. For industry, we hope AISHELL-2 recipe can be a helpful reference for building meaningful industrial systems and products.

Connected through hyperedge HE-1 (induced by source paper "speaker aware speech transformer"):
  with members:
    - "speaker adaptation for end to end ctc models"
    - "advancing acoustic to word ctc model"
    - "attention is all you need"
    - "embedding based speaker adaptive training of deep neural networks"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "espnet end to end speech processing toolkit"
    - "wav2letter the fastest open source speech recognition system"
    - "an online attention based model for speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"

Connected through hyperedge HE-3 (induced by source paper "unsupervised pre training for sequence to sequence speech recognition"):
  with members:
    - "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "atcspeech a multilingual pilot controller speech corpus from real air traffic control environment"):
  with members:
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "wav2letter the fastest open source speech recognition system"
    - "the airbus air traffic control speech recognition 2018 challenge towards atc automatic transcription and call sign detection"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "thchs 30 a free chinese speech corpus"

Connected through hyperedge HE-5 (induced by source paper "hi mia a far field text dependent speaker verification database and the baselines"):
  with members:
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "voxceleb2 deep speaker recognition"
    - "voxceleb a large scale speaker identification dataset"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-6 (induced by source paper "cantonese automatic speech recognition using transfer learning from mandarin"):
  with members:
    - "trivial transfer learning for low resource neural machine translation"
    - "transfer learning for speech recognition on a budget"
    - "transfer learning for speech and language processing"

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)