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 "streaming voice query recognition using causal convolutional recurrent neural networks"):
  Abstract: Voice-enabled commercial products are ubiquitous, typically enabled by lightweight on-device keyword spotting (KWS) and full automatic speech recognition (ASR) in the cloud. ASR systems require significant computational resources in training and for inference, not to mention copious amounts of annotated speech data. KWS systems, on the other hand, are less resource-intensive but have limited capabilities. On the Comcast Xfinity X1 entertainment platform, we explore a middle ground between ASR and KWS: We introduce a novel, resource-efficient neural network for voice query recognition that is much more accurate than state-of-the-art CNNs for KWS, yet can be easily trained and deployed with limited resources. On an evaluation dataset representing the top 200 voice queries, we achieve a low false alarm rate of 1% and a query error rate of 6%. Our model performs inference 8.24x faster than the current ASR system.

Includes members:
  Member "deep residual learning for small footprint keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "state of the art speech recognition with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "learning intrinsic sparse structures within long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully quantized transformer for improved translation")
  Member "convolutional recurrent neural networks for small footprint keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "orthogonality constrained multi head attention for keyword spotting")
  Member "comparing human and machine errors in conversational speech transcription":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr")
  Member "listening to the world improves speech command recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "symbolic inductive bias for visually grounded learning of spoken language")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "the microsoft 2016 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")

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)