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: "listening to the world improves speech command recognition"
  Abstract: We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands. Our key finding is that not only is it possible to transfer representations from an unrelated task like environmental sound classification to a voice-focused task like speech command recognition, but also that doing so improves accuracies significantly. We also investigate the effect of increased model capacity for transfer learning audio, by first validating known results from the field of Computer Vision of achieving better accuracies with increasingly deeper networks on two audio datasets: UrbanSound8k and the newly released Google Speech Commands dataset. Then we propose a simple multiscale input representation using dilated convolutions and show that it is able to aggregate larger contexts and increase classification performance. Further, the models trained using a combination of transfer learning and multiscale input representations need only 40% of the training data to achieve similar accuracies as a freshly trained model with 100% of the training data. Finally, we demonstrate a positive interaction effect for the multiscale input and transfer learning, making a case for the joint application of the two techniques.

Connected through hyperedge HE-1 (induced by source paper "symbolic inductive bias for visually grounded learning of spoken language"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "lessons learned in multilingual grounded language learning"
    - "the natural language decathlon multitask learning as question answering"
    - "syntactic scaffolds for semantic structures"
    - "sequence based multi lingual low resource speech recognition"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "scheduled multi task learning from syntax to translation"

Connected through hyperedge HE-2 (induced by source paper "hyperadam a learnable task adaptive adam for network training"):
  with members:
    - "on the convergence of adam and beyond"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "learning gradient descent better generalization and longer horizons"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"

Connected through hyperedge HE-3 (induced by source paper "streaming voice query recognition using causal convolutional recurrent neural networks"):
  with members:
    - "deep residual learning for small footprint keyword spotting"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning intrinsic sparse structures within long short term memory"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "comparing human and machine errors in conversational speech transcription"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the microsoft 2016 conversational speech recognition system"

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)