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: "aclnet efficient end to end audio classification cnn"
  Abstract: We propose an efficient end-to-end convolutional neural network architecture, AclNet, for audio classification. When trained with our data augmentation and regularization, we achieved state-of-the-art performance on the ESC-50 corpus with 85:65% accuracy. Our network allows configurations such that memory and compute requirements are drastically reduced, and a tradeoff analysis of accuracy and complexity is presented. The analysis shows high accuracy at significantly reduced computational complexity compared to existing solutions. For example, a configuration with only 155k parameters and 49:3 million multiply-adds per second is 81:75%, exceeding human accuracy of 81:3%. This improved efficiency can enable always-on inference in energy-efficient platforms.

Connected through hyperedge HE-1 (induced by source paper "leveraging topics and audio features with multimodal attention for audio visual scene aware dialog"):
  with members:
    - "audio visual scene aware dialog"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "attention is all you need"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "cnn architectures for large scale audio classification"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-2 (induced by source paper "exploring context attention and audio features for audio visual scene aware dialog"):
  with members:
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "cnn architectures for large scale audio classification"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "vqa visual question answering"
    - "video paragraph captioning using hierarchical recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "context attention and audio feature explorations for audio visual scene aware dialog"):
  with members:
    - "attention is all you need"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "vqa visual question answering"
    - "sequence to sequence video to text"
    - "video paragraph captioning using hierarchical recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "cider consensus based image description evaluation"

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)