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: "large scale weakly labeled semi supervised sound event detection in domestic environments"
  Abstract: This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of the systems is to provide not only the event class but also the event time boundaries given that multiple events can be present in an audio recording. Another challenge of the task is to explore the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly labeled training set to improve system performance. The data are Youtube video excerpts from domestic context which have many applications such as ambient assisted living. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events.. .) and potential industrial applications .

Connected through hyperedge HE-1 (induced by source paper "duration robust weakly supervised sound event detection"):
  with members:
    - "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection"
    - "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "hodge and podge hybrid supervised sound event detection with multi hot mixmatch and composition consistence training"):
  with members:
    - "hodgepodge sound event detection based on ensemble of semi supervised learning methods"
    - "mixmatch a holistic approach to semi supervised learning"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  with members:
    - "sound event detection using graph laplacian regularization based on event co occurrence"
    - "weakly labelled audioset tagging with attention neural networks"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "attention based deep multiple instance learning"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"

Connected through hyperedge HE-4 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "weakly labeled sound event detection using tri training and adversarial learning"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "audio event detection using weakly labeled data"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "what you need is a more professional teacher"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"
    - "fully convolutional multi class multiple instance learning"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "guided learning for weakly labeled semi supervised sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection"):
  with members:
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "language modeling with gated convolutional networks"
    - "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)