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: "gram ctc automatic unit selection and target decomposition for sequence labelling"
  Abstract: Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: 1) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and 2) the decomposition of target sequences is fixed. These drawbacks usually result in sub-optimal performance of modeling sequences. In this pa- per, we extend the popular CTC loss criterion to alleviate these limitations, and propose a new loss function called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically learns the best set of basic units (grams), as well as the most suitable decomposition of tar- get sequences. Unlike CTC, Gram-CTC allows the model to output variable number of characters at each time step, which enables the model to capture longer term dependency and improves the computational efficiency. We demonstrate that the proposed Gram-CTC improves CTC in terms of both performance and efficiency on the large vocabulary speech recognition task at multiple scales of data, and that with Gram-CTC we can outperform the state-of-the-art on a standard speech benchmark.

Connected through hyperedge HE-1 (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"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "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-2 (induced by source paper "towards using context dependent symbols in ctc without state tying decision trees"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep triphone embedding improves phoneme recognition"
    - "listen attend and spell"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "attention based models for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "advancing acoustic to word ctc model with attention and mixed units"):
  with members:
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-5 (induced by source paper "improved training of end to end attention models for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "letter based speech recognition with gated convnets"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-6 (induced by source paper "on the inductive bias of word character level multi task learning for speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "improving end to end speech recognition with policy learning"
    - "direct acoustics to word models for english conversational speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-7 (induced by source paper "advancing connectionist temporal classification with attention modeling"):
  with members:
    - "advancing acoustic to word ctc model"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

Connected through hyperedge HE-8 (induced by source paper "trace norm regularization and faster inference for embedded speech recognition rnns"):
  with members:
    - "factorization tricks for lstm networks"
    - "exploring sparsity in recurrent neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reexamining low rank matrix factorization for trace norm regularization"
    - "learning compact recurrent neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

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)