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: "distilling word embeddings an encoding approach"
  Abstract: Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.

Connected through hyperedge HE-1 (induced by source paper "bam born again multi task networks for natural language understanding"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multi task deep neural networks for natural language understanding"
    - "unifying question answering text classification and regression via span extraction"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unifying question answering and text classification via span extraction"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-2 (induced by source paper "model compression with two stage multi teacher knowledge distillation for web question answering system"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "bam born again multi task networks for natural language understanding"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "knowledge distillation in document retrieval"):
  with members:
    - "attention guided answer distillation for machine reading comprehension"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "reading wikipedia to answer open domain questions"
    - "sequence level knowledge distillation"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "the pupil has become the master teacher student model based word embedding distillation with ensemble learning"):
  with members:
    - "dynamic routing between capsules"
    - "enriching word vectors with subword information"
    - "deep model compression distilling knowledge from noisy teachers"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "transferring knowledge from a rnn to a dnn"
    - "fitnets hints for thin deep nets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "attention guided answer distillation for machine reading comprehension"):
  with members:
    - "robust machine comprehension models via adversarial training"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi range reasoning for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "data distillation towards omni supervised learning"
    - "the narrativeqa reading comprehension challenge"
    - "dcn mixed objective and deep residual coattention for question answering"

Connected through hyperedge HE-6 (induced by source paper "cross lingual distillation for text classification"):
  with members:
    - "sequence level knowledge distillation"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "semi supervised sequence learning"
    - "cross modal distillation for supervision transfer"
    - "an empirical study on sentiment classification of chinese review using word embedding"
    - "fitnets hints for thin deep nets"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "fine grained entity type classification by jointly learning representations and label embeddings"):
  with members:
    - "transfer learning for speech and language processing"
    - "adam a method for stochastic optimization"
    - "context dependent fine grained entity type tagging"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "sequence level knowledge distillation"):
  with members:
    - "character based neural machine translation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "learning compact recurrent neural networks"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "exploring the limits of language modeling"
    - "auto sizing neural networks with applications to n gram language models"
    - "multilingual language processing from bytes"

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)