human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  Abstract: Sound event detection (SED) consists in recognizing the presence of sound events in the segment of audio and detecting their onset as well as offset. In this paper, we focus on two common problems on SED: how to carry out efficient weakly-supervised learning and how to learn better from the unbalanced dataset in which multiple sound events often occur in co-occurrence. #R##N#We approach SED as a multiple instance learning (MIL) problem and utilize a neural network framework with different pooling modules to solve it. General MIL approaches includes two approaches: the instance-level approach and the embedding-level approach. Since the embedding-level approach tends to perform better than the instance-level approach in terms of bag-level classification but can not provide instance-level probabilities, we present how to generate instance-level probabilities for it. Moreover, we further propose a specialized decision surface (SDS) for the embedding-level attention pooling. We analyze and explained why an embedding-level attention module with SDS is better than other typical pooling modules from the perspective of the high-level feature space. As for the problem of unbalanced dataset and the co-occurrence of multiple categories in the polyphonic event detection task, we propose a disentangled feature (DF) to reduce interference among categories, which optimizes the high-level feature space by disentangling it based on class-wise identifiable information and obtaining multiple different subspaces. Experiments on the dataset of DCASE 2018 Task 4 show that the proposed SDS and DF significantly improve the detection performance of the embedding-level MIL approach with an attention pooling module and outperform the first place system in the challenge by 6.2 percentage points.

Includes members:
  Member "sound event detection using graph laplacian regularization based on event co occurrence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint analysis of acoustic events and scenes based on multitask learning")
  Member "weakly labelled audioset tagging with attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization")
  Member "sonyc a system for the monitoring analysis and mitigation of urban noise pollution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems")
  Member "large scale weakly labeled semi supervised sound event detection in domestic environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "duration robust weakly supervised sound event detection")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "duration robust weakly supervised sound event detection")
  Member "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling")
  Member "adaptive pooling operators for weakly labeled sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limitations of weak labels for embedding and tagging")

Question: Please tell me which class the source paper 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)