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: "weakly supervised scalable audio content analysis"
  Abstract: Audio Event Detection is an important task for content analysis of multimedia data. Most of the current works on detection of audio events is driven through supervised learning approaches. We propose a weakly supervised learning framework which can make use of the tremendous amount of web multimedia data with significantly reduced annotation effort and expense. Specifically, we use several multiple instance learning algorithms to show that audio event detection through weak labels is feasible. We also propose a novel scalable multiple instance learning algorithm and show that its competitive with other multiple instance learning algorithms for audio event detection tasks.

Connected through hyperedge HE-1 (induced by source paper "a closer look at weak label learning for audio events"):
  with members:
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "audio event detection using weakly labeled data"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "multiple instance learning a survey of problem characteristics and applications"):
  with members:
    - "feature learning from spectrograms for assessment of personality traits"
    - "risk minimization framework for multiple instance learning from positive and unlabeled bags"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "label stability in multiple instance learning"
    - "characterizing multiple instance datasets"
    - "a multi scale multiple instance video description network"
    - "detector discovery in the wild joint multiple instance and representation learning"
    - "on learning to localize objects with minimal supervision"

Connected through hyperedge HE-3 (induced by source paper "discovering sound concepts and acoustic relations in text"):
  with members:
    - "audio event detection using weakly labeled data"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "an approach for self training audio event detectors using web data"):
  with members:
    - "experiments on the dcase challenge 2016 acoustic scene classification and sound event detection in real life recording"
    - "audio event detection using weakly labeled data"
    - "sound representation and classification benchmark for domestic robots"

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)