human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition"):
  Abstract: End-to-end neural network systems for automatic speech recognition (ASR) are trained from acoustic features to text transcriptions. In contrast to modular ASR systems, which contain separately-trained components for acoustic modeling, pronunciation lexicon, and language modeling, the end-to-end paradigm is both conceptually simpler and has the potential benefit of training the entire system on the end task. However, such neural network models are more opaque: it is not clear how to interpret the role of different parts of the network and what information it learns during training. In this paper, we analyze the learned internal representations in an end-to-end ASR model. We evaluate the representation quality in terms of several classification tasks, comparing phonemes and graphemes, as well as different articulatory features. We study two languages (English and Arabic) and three datasets, finding remarkable consistency in how different properties are represented in different layers of the deep neural network.

Includes members:
  Member "towards end to end code switching speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data")
  Member "analyzing learned representations of a deep asr performance prediction model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "untangling in invariant speech recognition")
  Member "stream attention based multi array end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance monitoring for end to end speech recognition")
  Member "phonetic and graphemic systems for multi genre broadcast transcription":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "darts dialectal arabic transcription system")
  Member "analysis methods in neural language processing a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a kernel of truth determining rumor veracity on twitter by diffusion pattern alone")
  Member "analyzing hidden representations in end to end automatic speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "untangling in invariant speech recognition")
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "untangling in invariant speech recognition")

Question: Please tell me which class the source paper 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)