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: "asr error management for improving spoken language understanding"
  Abstract: This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .

Connected through hyperedge HE-1 (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"
    - "towards end to end spoken language understanding"
    - "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"
    - "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-2 (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"
    - "towards end to end spoken language understanding"
    - "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"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reading wikipedia to answer open domain questions"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "hierarchical attention model for improved machine comprehension of spoken content"

Connected through hyperedge HE-4 (induced by source paper "mitigating the impact of speech recognition errors on chatbot using sequence to sequence model"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "abstractive headline generation for spoken content by attentive recurrent neural networks with asr error modeling"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "deep domain confusion maximizing for domain invariance"

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)