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 "improving gated recurrent unit based acoustic modeling with batch normalization and enlarged context"):
  Abstract: The use of future contextual information is typically shown to be helpful for acoustic modeling. Recently, we proposed a RNN model called minimal gated recurrent unit with input projection (mGRUIP), in which a context module namely temporal convolution, is specifically designed to model the future context. This model, mGRUIP with context module (mGRUIP-Ctx), has been shown to be able of utilizing the future context effectively, meanwhile with quite low model latency and computation cost. In this paper, we continue to improve mGRUIP-Ctx with two revisions: applying BN methods and enlarging model context. Experimental results on two Mandarin ASR tasks (8400 hours and 60K hours) show that, the revised mGRUIP-Ctx outperform LSTM with a large margin (11% to 38%). It even performs slightly better than a superior BLSTM on the 8400h task, with 33M less parameters and just 290ms model latency.

Includes members:
  Member "deep fsmn for large vocabulary continuous speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel pyramidal fsmn architecture with lattice free mmi for speech recognition")
  Member "gated recurrent unit based acoustic modeling with future context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aishell 2 transforming mandarin asr research into industrial scale")
  Member "improving speech recognition by revising gated recurrent units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech")
  Member "aishell 1 an open source mandarin speech corpus and a speech recognition baseline":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speaker aware speech transformer")
  Member "recurrent batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking batch normalization in transformers")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "feedforward sequential memory networks a new structure to learn long term dependency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep fsmn for large vocabulary continuous speech recognition")
  Member "highway long short term memory rnns for distant speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

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)