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: "exploring neural transducers for end to end speech recognition"
  Abstract: In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue - RNNTransducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models - when all encoder layers are forward only, and when encoders downsample the input representation aggressively.

Connected through hyperedge HE-1 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "mlperf training benchmark"
    - "recnmp accelerating personalized recommendation with near memory processing"
    - "bandana using non volatile memory for storing deep learning models"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "tbd benchmarking and analyzing deep neural network training"

Connected through hyperedge HE-2 (induced by source paper "neural image compression and explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "global model interpretation via recursive partitioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "sirenattack generating adversarial audio for end to end acoustic systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "dual path networks"
    - "crafting adversarial examples for speech paralinguistics applications"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-4 (induced by source paper "beyond human level accuracy computational challenges in deep learning"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "deep learning scaling is predictable empirically"

Connected through hyperedge HE-5 (induced by source paper "omninet a unified architecture for multi modal multi task learning"):
  with members:
    - "bilinear attention networks"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "tvqa localized compositional video question answering"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-6 (induced by source paper "on the choice of modeling unit for sequence to sequence speech recognition"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "the capio 2017 conversational speech recognition system"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "optimal completion distillation for sequence learning"
    - "sequence to sequence models can directly translate foreign speech"
    - "state of the art speech recognition with sequence to sequence models"
    - "minimum word error rate training for attention based sequence to sequence models"

Connected through hyperedge HE-7 (induced by source paper "rwth asr systems for librispeech hybrid vs attention w o data augmentation"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "language modeling with deep transformers"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "improving rnn transducer modeling for end to end speech recognition"):
  with members:
    - "advancing acoustic to word ctc model with attention and mixed units"
    - "advancing acoustic to word ctc model"
    - "layer trajectory lstm"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "monotonic chunkwise attention"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "improving the performance of online neural transducer models"

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)