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: "phonetically oriented word error alignment for speech recognition error analysis in speech translation"
  Abstract: We propose a variation to the commonly used Word Error Rate (WER) metric for speech recognition evaluation which incorporates the alignment of phonemes, in the absence of time boundary information. After computing the Levenshtein alignment on words in the reference and hypothesis transcripts, spans of adjacent errors are converted into phonemes with word and syllable boundaries and a phonetic Levenshtein alignment is performed. The phoneme alignment information is used to correct the word alignment labels in each error region. We demonstrate that our Phonetically-Oriented Word Error Rate (POWER) yields similar scores to WER with the added advantages of better word alignments and the ability to capture one-to-many alignments corresponding to homophonic errors in speech recognition hypotheses. These improved alignments allow us to better trace the impact of Levenshtein error types in speech recognition on downstream tasks such as speech translation.

Connected through hyperedge HE-1 (induced by source paper "machine translation in pronunciation space"):
  with members:
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"
    - "learning variable length units for smt between related languages via byte pair encoding"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "assessing the tolerance of neural machine translation systems against speech recognition errors"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "assessing the tolerance of neural machine translation systems against speech recognition errors"):
  with members:
    - "neural versus phrase based machine translation quality a case study"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

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)