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: "knowledge distillation for small footprint highway networks"
  Abstract: Deep learning has significantly advanced state-of-the-art of speech recognition in the past few years. However, compared to conventional Gaussian mixture acoustic models, neural network models are usually much larger, and are therefore not very deployable in embedded devices. Previously, we investigated a compact highway deep neural network (HDNN) for acoustic modelling, which is a type of depth-gated feedforward neural network. We have shown that HDNN-based acoustic models can achieve comparable recognition accuracy with much smaller number of model parameters compared to plain deep neural network (DNN) acoustic models. In this paper, we push the boundary further by leveraging on the knowledge distillation technique that is also known as {\it teacher-student} training, i.e., we train the compact HDNN model with the supervision of a high accuracy cumbersome model. Furthermore, we also investigate sequence training and adaptation in the context of teacher-student training. Our experiments were performed on the AMI meeting speech recognition corpus. With this technique, we significantly improved the recognition accuracy of the HDNN acoustic model with less than 0.8 million parameters, and narrowed the gap between this model and the plain DNN with 30 million parameters.

Connected through hyperedge HE-1 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "fast intent classification for spoken language understanding"):
  with members:
    - "one vs all models for asynchronous training an empirical analysis"
    - "statistical model compression for small footprint natural language understanding"
    - "a re ranker scheme for integrating large scale nlu models"
    - "spoken language understanding on the edge"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "developing far field speaker system via teacher student learning"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "lessons from building acoustic models with a million hours of speech"):
  with members:
    - "on calibration of modern neural networks"
    - "large scale domain adaptation via teacher student learning"
    - "empirical evaluation of parallel training algorithms on acoustic modeling"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "large scale distributed acoustic modeling with back off rm n grams"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-5 (induced by source paper "small footprint highway deep neural networks for speech recognition"):
  with members:
    - "sequence training and adaptation of highway deep neural networks"
    - "recurrent highway networks"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "transferring knowledge from a rnn to a dnn"

Connected through hyperedge HE-6 (induced by source paper "toward computation and memory efficient neural network acoustic models with binary weights and activations"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "small footprint deep neural networks with highway connections for speech recognition"
    - "training very deep networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "large scale domain adaptation via teacher student learning"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

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.CL(Computation and Language)