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: "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
  Abstract: Sound event detection (SED) entails two subtasks: recognizing what types of sound events are present in an audio stream (audio tagging), and pinpointing their onset and offset times (localization). In the popular multiple instance learning (MIL) framework for SED with weak labeling, an important component is the pooling function. This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization. Although the attention pooling function is currently receiving the most attention, we find the linear softmax pooling function to perform the best among the five. Using this pooling function, we build a neural network called TALNet. It is the first system to reach state-of-the-art audio tagging performance on Audio Set, while exhibiting strong localization performance on the DCASE 2017 challenge at the same time.

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"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "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 "what makes training multi modal classification networks hard"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hypothesis only baselines in natural language inference"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "slowfast networks for video recognition"
    - "the sound of pixels"

Connected through hyperedge HE-3 (induced by source paper "what makes training multi modal networks hard"):
  with members:
    - "deep hough voting for 3d object detection in point clouds"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-4 (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"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "attention based deep multiple instance learning"
    - "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-5 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "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-6 (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"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-7 (induced by source paper "secost sequential co supervision for weakly labeled audio event detection"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "continual lifelong learning with neural networks a review"
    - "weakly labelled audioset tagging with attention neural networks"
    - "learning sound events from webly labeled data"
    - "a closer look at weak label learning for audio events"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-8 (induced by source paper "extracting 2d weak labels from volume labels using multiple instance learning in ct hemorrhage detection"):
  with members:
    - "distributed deep learning for robust multi site segmentation of ct imaging after traumatic brain injury"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "3d anisotropic hybrid network transferring convolutional features from 2d images to 3d anisotropic volumes"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "tensorflow a system for large scale machine learning"

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)