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: "kaldi pdnn building dnn based asr systems with kaldi and pdnn"
  Abstract: The Kaldi toolkit is becoming popular for constructing automated speech recognition (ASR) systems. Meanwhile, in recent years, deep neural networks (DNNs) have shown state-of-the-art performance on various ASR tasks. This document describes our open-source recipes to implement fully-fledged DNN acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning toolkit developed under the Theano environment. Using these recipes, we can build up multiple systems including DNN hybrid systems, convolutional neural network (CNN) systems and bottleneck feature systems. These recipes are directly based on the Kaldi Switchboard 110-hour setup. However, adapting them to new datasets is easy to achieve.

Connected through hyperedge HE-1 (induced by source paper "densely connected convolutional networks for speech recognition"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "very deep convolutional networks for large scale image recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "simplified end to end mmi training and voting for asr"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "wav2letter an end to end convnet based speech recognition system"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "personalized speech recognition on mobile devices"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "achieving human parity in conversational speech recognition"
    - "listen attend and spell"

Connected through hyperedge HE-3 (induced by source paper "visual features for context aware speech recognition"):
  with members:
    - "multi30k multilingual english german image descriptions"
    - "deep residual learning for image recognition"
    - "translating videos to natural language using deep recurrent neural networks"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "multi task learning of deep neural networks for audio visual automatic speech recognition"):
  with members:
    - "deep multimodal learning for audio visual speech recognition"

Connected through hyperedge HE-5 (induced by source paper "audio visual speech recognition using deep recurrent neural networks"):
  with members:
    - "lipreading with long short term memory"
    - "deep multimodal learning for audio visual speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-6 (induced by source paper "environmental noise embeddings for robust speech recognition"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

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.LG(Machine Learning)