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 "differentiable pooling for unsupervised acoustic model adaptation"):
  Abstract: We present a deep neural network (DNN) acoustic model that includes parametrised and differentiable pooling operators. Unsupervised acoustic model adaptation is cast as the problem of updating the decision boundaries implemented by each pooling operator. In particular, we experiment with two types of pooling parametrisations: learned   $L_p$  -norm pooling and weighted Gaussian pooling, in which the weights of both operators are treated as speaker-dependent. We perform investigations using three different large vocabulary speech recognition corpora: AMI meetings, TED talks, and Switchboard conversational telephone speech. We demonstrate that differentiable pooling operators provide a robust and relatively low-dimensional way to adapt acoustic models, with relative word error rates reductions ranging from 5–20% with respect to unadapted systems, which themselves are better than the baseline fully-connected DNN-based acoustic models. We also investigate how the proposed techniques work under various adaptation conditions including the quality of adaptation data and complementarity to other feature- and model-space adaptation methods, as well as providing an analysis of the characteristics of each of the proposed approaches.

Includes members:
  Member "learning hidden unit contributions for unsupervised acoustic model adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speaker adaptation for end to end ctc models")
  Member "maximum a posteriori adaptation of network parameters in deep models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speaker adaptation for attention based end to end speech recognition")
  Member "learned norm pooling for deep feedforward and recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised segmentation of cracks on solar cells using normalized lp norm")
  Member "improvements to deep convolutional neural networks for lvcsr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for audio signal processing")
  Member "feature learning in deep neural networks studies on speech recognition tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent weights do not exist rethinking binarized neural network optimization")
  Member "differentiable pooling for hierarchical feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive pooling operators for weakly labeled sound event detection")
  Member "convolutional neural networks applied to house numbers digit classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional networks with dense connectivity")

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)