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 "end to end anchored speech recognition"):
  Abstract: Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering background speech, i.e., background noise and interfering speech from another person or media device in proximity need to be ignored. We propose two end-to-end models to tackle this problem with information extracted from the "anchored segment". The anchored segment refers to the wake-up word part of an audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise. The first method is called "Multi-source Attention" where the attention mechanism takes both the speaker information and decoder state into consideration. The second method directly learns a frame-level mask on top of the encoder output. We also explore a multi-task learning setup where we use the ground truth of the mask to guide the learner. Given that audio data with interfering speech is rare in our training data set, we also propose a way to synthesize "noisy" speech from "clean" speech to mitigate the mismatch between training and test data. Our proposed methods show up to 15% relative reduction in WER for Amazon Alexa live data with interfering background speech without significantly degrading on clean speech.

Includes members:
  Member "mixed precision training for nlp and speech recognition with openseq2seq":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pytorchpipe a framework for rapid prototyping of pipelines combining language and vision")
  Member "progressive joint modeling in unsupervised single channel overlapped speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  Member "end to end contextual speech recognition using class language models and a token passing decoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio caption listen and tell")
  Member "on modular training of neural acoustics to word model for lvcsr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learned in speech recognition contextual acoustic word embeddings")
  Member "deep extractor network for target speaker recovery from single channel speech mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "state of the art speech recognition with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "end to end text dependent speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning")
  Member "attention based models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient content based sparse attention with routing transformers")

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)