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 asr free keyword search from speech"):
  Abstract: Conventional keyword search (KWS) systems for speech databases match the input text query to the set of word hypotheses generated by an automatic speech recognition (ASR) system from utterances in the database. Hence, such KWS systems attempt to solve the complex problem of ASR as a precursor. Training an ASR system itself is a time-consuming process requiring transcribed speech data. Our prior work presented an ASR-free end-to-end system that needed minimal supervision and trained significantly faster than an ASR-based KWS system. The ASR-free KWS system consisted of three subsystems. The first subsystem was a recurrent neural network based acoustic encoder that extracted a finite-dimensional embedding of the speech utterance. The second subsystem was a query encoder that produced an embedding of the input text query. The acoustic and query embeddings were input to a feedforward neural network that predicted whether the query occurred in the acoustic utterance or not. This paper extends our prior work in several ways. First, we significantly improve upon our previous ASR-free KWS results by nearly 20% relative through improvements to the acoustic encoder. Next, we show that it is possible to train the acoustic encoder on languages other than the language of interest with only a small drop in KWS performance. Finally, we attempt to predict the location of the detected keywords by training a location-sensitive KWS network.

Includes members:
  Member "direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "acoustic to word models with conversational context information")
  Member "the ibm 2016 english conversational telephone speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes")
  Member "the microsoft 2016 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "multi view recurrent neural acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effectiveness of self supervised pre training for speech recognition")
  Member "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving automated segmentation of radio shows with audio embeddings")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for audio signal processing")
  Member "fast and accurate recurrent neural network acoustic models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")

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)