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: "what makes audio event detection harder than classification"
  Abstract: There is a common observation that audio event classification is easier to deal with than detection. So far, this observation has been accepted as a fact and we lack of a careful analysis. In this paper, we reason the rationale behind this fact and, more importantly, leverage them to benefit the audio event detection task. We present an improved detection pipeline in which a verification step is appended to augment a detection system. This step employs a high-quality event classifier to postprocess the benign event hypotheses outputted by the detection system and reject false alarms. To demonstrate the effectiveness of the proposed pipeline, we implement and pair up different event detectors based on the most common detection schemes and various event classifiers, ranging from the standard bag-of-words model to the state-of-the-art bank-of-regressors one. Experimental results on the ITC-Irst dataset show significant improvements to detection performance. More importantly, these improvements are consistent for all detector-classifier combinations.

Connected through hyperedge HE-1 (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"
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "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"
    - "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-2 (induced by source paper "enabling early audio event detection with neural networks"):
  with members:
    - "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"
    - "an overview of multi task learning in deep neural networks"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

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)