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: "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
  Abstract: This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the category of an audio clip from a subset of 41 diverse categories drawn from the AudioSet Ontology. We present the task, the dataset prepared for the competition, and a baseline system.

Connected through hyperedge HE-1 (induced by source paper "multi representation knowledge distillation for audio classification"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "paraphrasing complex network network compression via factor transfer"
    - "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"
    - "large scale distributed neural network training through online distillation"
    - "sample mixed based data augmentation for domestic audio tagging"
    - "deep mutual learning"
    - "mixup beyond empirical risk minimization"
    - "cooperative learning with visual attributes"

Connected through hyperedge HE-2 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  with members:
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-3 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "a structured self attentive sentence embedding"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "a joint detection classification model for audio tagging of weakly labelled data"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-4 (induced by source paper "panns large scale pretrained audio neural networks for audio pattern recognition"):
  with members:
    - "musicnn pre trained convolutional neural networks for music audio tagging"
    - "bottom up broadcast neural network for music genre classification"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-5 (induced by source paper "model agnostic approaches to handling noisy labels when training sound event classifiers"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "audio tagging with noisy labels and minimal supervision"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "learning sound events from webly labeled data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "audio tagging with noisy labels and minimal supervision"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "model agnostic approaches to handling noisy labels when training sound event classifiers"
    - "do imagenet classifiers generalize to imagenet"
    - "facilitating the manual annotation of sounds when using large taxonomies"
    - "learning sound events from webly labeled data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "polyphonic sound event detection and localization using a two stage strategy"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "indoor sound source localization with probabilistic neural network"
    - "deep neural networks for multiple speaker detection and localization"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "sound source localization in a multipath environment using convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial attacks in sound event classification"):
  with members:
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "mixup beyond empirical risk minimization"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "cnn architectures for large scale audio classification"
    - "language modeling with gated convolutional networks"
    - "densely connected convolutional networks"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"

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)