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: "r crnn region based convolutional recurrent neural network for audio event detection"
  Abstract: This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). The proposed network is inspired by Faster-RCNN, a well known region-based convolutional network framework for visual object detection. Different from the original Faster-RCNN, a recurrent layer is added on top of the convolutional network to capture the long-term temporal context from the extracted high level features. While most of the previous works on AED generate predictions at frame level first, and then use post-processing to predict the onset/offset timestamps of events from a probability sequence; the proposed method generates predictions at event level directly and can be trained end-to-end with a multitask loss, which optimizes the classification and localization of audio events simultaneously. The proposed method is tested on DCASE 2017 Challenge dataset. To the best of our knowledge, R-CRNN is the best performing single-model method among all methods without using ensembles both on development and evaluation sets. Compared to the other region-based network for AED (R-FCN) with an event-based error rate (ER) of 0.18 on the development set, our method reduced the ER to half.

Connected through hyperedge HE-1 (induced by source paper "multi scale time frequency attention for acoustic event detection"):
  with members:
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "learning how to listen a temporal frequential attention model for sound event detection"
    - "a simple model for detection of rare sound events"
    - "residual attention network for image classification"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi scale time frequency attention for rare sound event detection"):
  with members:
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "learning how to listen a temporal frequential attention model for sound event detection"
    - "a simple model for detection of rare sound events"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "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 "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "enabling early audio event detection with neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "dnn and cnn with weighted and multi task loss functions for audio event detection"
    - "what makes audio event detection harder than classification"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-4 (induced by source paper "learning how to listen a temporal frequential attention model for sound event detection"):
  with members:
    - "attention is all you need"
    - "audio set classification with attention model a probabilistic perspective"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)