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 "learning to detect keyword parts and whole by smoothed max pooling"):
  Abstract: We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR (Large vocabulary continuous speech recognition), making further optimization possible. The proposed system outperforms the baseline keyword spotting model in [1] due to increased optimizability. Further, it can be more easily adapted for on-device learning applications due to reduced dependency on LVCSR.

Includes members:
  Member "streaming end to end speech recognition for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "end to end streaming keyword spotting":
    also connected through other hyperedges:
      (none)
  Member "streaming small footprint keyword spotting using sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "a cascade architecture for keyword spotting on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end streaming keyword spotting")
  Member "small footprint keyword spotting using deep neural network and connectionist temporal classifier":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal convolution for real time keyword spotting on mobile devices")
  Member "max pooling loss training of long short term memory networks for small footprint keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)