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 "secost sequential co supervision for weakly labeled audio event detection"):
  Abstract: Weakly supervised learning algorithms are critical for scaling audio event detection to several hundreds of sound categories. Such learning models should not only disambiguate sound events efficiently with minimal class-specific annotation but also be robust to label noise, which is more apparent with weak labels instead of strong annotations. In this work, we propose a new framework for designing learning models with weak supervision by bridging ideas from sequential learning and knowledge distillation. We refer to the proposed methodology as SeCoST (pronounced Sequest) --- Sequential Co-supervision for training generations of Students. SeCoST incrementally builds a cascade of student-teacher pairs via a novel knowledge transfer method. Our evaluations on Audioset (the largest weakly labeled dataset available) show that SeCoST achieves a mean average precision of 0.383 while outperforming prior state of the art by a considerable margin.

Includes members:
  Member "learning sound event classifiers from web audio with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning with out of distribution data for audio classification")
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "weakly labelled audioset tagging with attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")
  Member "learning sound events from webly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "model agnostic approaches to handling noisy labels when training sound event classifiers")
  Member "a closer look at weak label learning for audio events":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limitations of weak labels for embedding and tagging")
  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 "adaptive pooling operators for weakly labeled sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")

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)