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: "data efficient weakly supervised learning for low resource audio event detection using deep learning"
  Abstract: We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network. This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning. We successfully test our approach on two low-resource datasets that lack temporal labels.

Connected through hyperedge HE-1 (induced by source paper "learning sound event classifiers from web audio with noisy labels"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "a closer look at weak label learning for audio events"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-2 (induced by source paper "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection"):
  with members:
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "language modeling with gated convolutional networks"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-3 (induced by source paper "training neural audio classifiers with few data"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "deep learning for audio transcription on low resource datasets"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "unsupervised learning of semantic audio representations"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"

Connected through hyperedge HE-4 (induced by source paper "nips4bplus a richly annotated birdsong audio dataset"):
  with members:
    - "densely connected cnns for bird audio detection"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "audio event detection using weakly labeled data"

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)