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: "assessing the tolerance of neural machine translation systems against speech recognition errors"
  Abstract: Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine translation systems on textual inputs is actively researched in the literature , little has been discovered about the complexities of translating spoken language data with neural models. We introduce and motivate interesting problems one faces when considering the translation of automatic speech recognition (ASR) outputs on neural machine translation (NMT) systems. We test the robustness of sentence encoding approaches for NMT encoder-decoder modeling, focusing on word-based over byte-pair encoding. We compare the translation of utterances containing ASR errors in state-of-the-art NMT encoder-decoder systems against a strong phrase-based machine translation baseline in order to better understand which phenomena present in ASR outputs are better represented under the NMT framework than approaches that represent translation as a linear model.

Connected through hyperedge HE-1 (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"
    - "towards end to end spoken language understanding"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "robust neural machine translation for clean and noisy speech transcripts"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "punctuation prediction model for conversational speech"
    - "attention is all you need"
    - "mitigating the impact of speech recognition errors on chatbot using sequence to sequence model"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "robust neural machine translation with joint textual and phonetic embedding"):
  with members:
    - "towards robust neural machine translation"
    - "improving the robustness of speech translation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"
    - "convolutional sequence to sequence learning"
    - "effective approaches to attention based neural machine translation"
    - "phonetically oriented word error alignment for speech recognition error analysis in speech translation"

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)