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: "attention based fully convolutional network for speech emotion recognition"
  Abstract: Speech emotion recognition is a challenging task for three main reasons: 1) human emotion is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be detected in some specific moments during a long utterance; 3) speech data with emotional labeling is usually limited. In this paper, we present a novel attention based fully convolutional network for speech emotion recognition. We employ fully convolutional network as it is able to handle variable-length speech, free of the demand of segmentation to keep critical information not lost. The proposed attention mechanism can make our model be aware of which time-frequency region of speech spectrogram is more emotion-relevant. Considering limited data, the transfer learning is also adapted to improve the accuracy. Especially, it's interesting to observe obvious improvement obtained with natural scene image based pre-trained model. Validated on the publicly available IEMOCAP corpus, the proposed model outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and an unweighted accuracy of 63.9% respectively.

Connected through hyperedge HE-1 (induced by source paper "robust speaker recognition using speech enhancement and attention model"):
  with members:
    - "audio visual speech enhancement using conditional variational auto encoder"
    - "self multi head attention for speaker recognition"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
    - "cbam convolutional block attention module"
    - "voxceleb a large scale speaker identification dataset"
    - "squeeze and excitation networks"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-2 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "flightnns lightweight quantized deep neural networks for fast and accurate inference"
    - "structured knowledge distillation for dense prediction"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "writer aware cnn for parsimonious hmm based offline handwritten chinese text recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "deep fusion an attention guided factorized bilinear pooling for audio video emotion recognition"):
  with members:
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "factorized bilinear models for image recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional 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.SD(Sound)