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: "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
  Abstract: One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.

Connected through hyperedge HE-1 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  with members:
    - "enhancing sound texture in cnn based acoustic scene classification"
    - "acoustic scene classification by implicitly identifying distinct sound events"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "spatio temporal attention pooling for audio scene classification"
    - "enabling early audio event detection with neural networks"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "acoustic scene classification by implicitly identifying distinct sound events"):
  with members:
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "a hybrid approach with multi channel i vectors and convolutional neural networks for acoustic scene classification"
    - "audio event detection using weakly labeled data"
    - "acoustic scene classification classifying environments from the sounds they produce"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"):
  with members:
    - "learning audio sequence representations for acoustic event classification"
    - "rank pooling for action recognition"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

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)