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 "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  Abstract: We explore a keyword-based spoken language understanding system, in which the intent of the user can directly be derived from the detection of a sequence of keywords in the query. In this paper, we focus on an open-vocabulary keyword spotting method, allowing the user to define their own keywords without having to retrain the whole model. We describe the different design choices leading to a fast and small-footprint system, able to run on tiny devices, for any arbitrary set of user-defined keywords, without training data specific to those keywords. The model, based on a quantized long short-term memory (LSTM) neural network, trained with connectionist temporal classification (CTC), weighs less than 500KB. Our approach takes advantage of some properties of the predictions of CTC-trained networks to calibrate the confidence scores and implement a fast detection algorithm. The proposed system outperforms a standard keyword-filler model approach.

Includes members:
  Member "an optimized recurrent unit for ultra low power keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tiny eats eating detection on a microcontroller")
  Member "streaming end to end speech recognition for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "donut ctc based query by example keyword spotting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting")
  Member "efficient keyword spotting using dilated convolutions and gating":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ternary hybrid neural tree networks for highly constrained iot applications")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "automatic grammar augmentation for robust voice command recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting")
  Member "pyroomacoustics a python package for audio room simulation and array processing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial music real world audio adversary against wake word detection system")
  Member "spoken language understanding on the edge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting detection filters for small footprint open vocabulary keyword spotting")

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)