human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "emotion recognition from speech with recurrent neural networks"
  Abstract: In this paper the task of emotion recognition from speech is considered. Proposed approach uses deep recurrent neural network trained on a sequence of acoustic features calculated over small speech intervals. At the same time special probabilistic-nature CTC loss function allows to consider long utterances containing both emotional and neutral parts. The effectiveness of such an approach is shown in two ways. Firstly, the comparison with recent advances in this field is carried out. Secondly, human performance on the same task is measured. Both criteria show the high quality of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learning discriminative features using center loss and reconstruction as regularizer for speech emotion recognition"):
  with members:
    - "auxiliary tasks in multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-2 (induced by source paper "reusing neural speech representations for auditory emotion recognition"):
  with members:
    - "progressive neural networks"
    - "beyond fine tuning a modular approach to learning on small data"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "a deep neural network sentence level classification method with context information"):
  with members:
    - "show attend and tell neural image caption generation with visual attention"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "multi modal emotion recognition on iemocap dataset using deep learning"):
  with members:
    - "reusing neural speech representations for auditory emotion recognition"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"):
  with members:
    - "exploring different dimensions of attention for uncertainty detection"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "encoding source language with convolutional neural network for machine translation"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "generating sequences with recurrent neural networks"

Question: Please tell me which class the center node 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)