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: "residual convolutional ctc networks for automatic speech recognition"
  Abstract: Deep learning approaches have been widely used in Automatic Speech Recognition (ASR) and they have achieved a significant accuracy improvement. Especially, Convolutional Neural Networks (CNNs) have been revisited in ASR recently. However, most CNNs used in existing work have less than 10 layers which may not be deep enough to capture all human speech signal information. In this paper, we propose a novel deep and wide CNN architecture denoted as RCNN-CTC, which has residual connections and Connectionist Temporal Classification (CTC) loss function. RCNN-CTC is an end-to-end system which can exploit temporal and spectral structures of speech signals simultaneously. Furthermore, we introduce a CTC-based system combination, which is different from the conventional frame-wise senone-based one. The basic subsystems adopted in the combination are different types and thus mutually complementary to each other. Experimental results show that our proposed single system RCNN-CTC can achieve the lowest word error rate (WER) on WSJ and Tencent Chat data sets, compared to several widely used neural network systems in ASR. In addition, the proposed system combination can offer a further error reduction on these two data sets, resulting in relative WER reductions of $14.91\%$ and $6.52\%$ on WSJ dev93 and Tencent Chat data sets respectively.

Connected through hyperedge HE-1 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "black box adversarial attacks on video recognition models"
    - "adversarial t shirt evading person detectors in a physical world"
    - "semantic adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-2 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-3 (induced by source paper "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  with members:
    - "improved training for online end to end speech recognition systems"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "simplified end to end mmi training and voting for asr"
    - "advances in all neural speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-4 (induced by source paper "two stage training for chinese dialect recognition"):
  with members:
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "speaker embedding extraction with phonetic information"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "phonetic temporal neural model for language identification"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-6 (induced by source paper "deep lip reading a comparison of models and an online application"):
  with members:
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "improved training for online end to end speech recognition systems"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"
    - "convolutional sequence to sequence learning"
    - "depthwise separable convolutions for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "multi encoder multi resolution framework for end to end speech recognition"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "end to end speech recognition with word based rnn language models"
    - "improved regularization techniques for end to end speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-8 (induced by source paper "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "mixup beyond empirical risk minimization"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "countering adversarial images using input transformations"
    - "mask r cnn"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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)