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: "connectionist temporal localization for sound event detection with sequential labeling"
  Abstract: Research on sound event detection (SED) with weak labeling has mostly focused on presence/absence labeling, which provides no temporal information at all about the event occurrences. In this paper, we consider SED with sequential labeling, which specifies the temporal order of the event boundaries. The conventional connectionist temporal classification (CTC) framework, when applied to SED with sequential labeling, does not localize long events well due to a "peak clustering" problem. We adapt the CTC framework and propose connectionist temporal localization (CTL), which successfully solves the problem. Evaluation on a subset of Audio Set shows that CTL closes a third of the gap between presence/ absence labeling and strong labeling, demonstrating the usefulness of the extra temporal information in sequential labeling. CTL also makes it easy to combine sequential labeling with presence/absence labeling and strong labeling.

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"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "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 "duration robust sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "polyphonic audio tagging with sequentially labelled data using crnn with learnable gated linear units"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "language modeling with gated convolutional networks"
    - "a light cnn for deep face representation with noisy labels"

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)