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: "towards end to end spoken language understanding"
  Abstract: Spoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for down-streaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.

Connected through hyperedge HE-1 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "context aware neural based dialog act classification on automatically generated transcriptions"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "probing for semantic classes diagnosing the meaning content of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "a data efficient end to end spoken language understanding architecture"):
  with members:
    - "hybrid neural models for sequence modelling the best of three worlds"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "asr error management for improving spoken language understanding"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-3 (induced by source paper "pre training for query rewriting in a spoken language understanding system"):
  with members:
    - "knowledge distillation from internal representations"
    - "training neural response selection for task oriented dialogue systems"
    - "feedback based self learning in large scale conversational ai agents"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-4 (induced by source paper "modeling asr ambiguity for dialogue state tracking using word confusion networks"):
  with members:
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "towards universal dialogue state tracking"
    - "neural belief tracker data driven dialogue state tracking"
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

Connected through hyperedge HE-5 (induced by source paper "breaking the data barrier towards robust speech translation via adversarial stability training"):
  with members:
    - "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"
    - "towards robust neural machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "improving the robustness of speech translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "m2h gan a gan based mapping from machine to human transcripts for speech understanding"):
  with members:
    - "adversarial text generation without reinforcement learning"
    - "adversarial neural machine translation"
    - "adversarial generation of natural language"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial learning for neural dialogue generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-7 (induced by source paper "towards debugging deep neural networks by generating speech utterances"):
  with members:
    - "staircase network structural language identification via hierarchical attentive units"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "wavenet a generative model for raw audio"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"):
  with members:
    - "competence based curriculum learning for neural machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sequence to sequence models can directly translate foreign speech"
    - "asr error management for improving spoken language understanding"
    - "deep speech scaling up end to end speech recognition"

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)