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: "fasttext zip compressing text classification models"
  Abstract: We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.  After considering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store the word embeddings.  While the original technique leads to a loss in accuracy,  we adapt this method to circumvent the quantization artifacts. As a result, our approach produces a text classifier, derived from the fastText approach, which at test time requires only a fraction of the memory compared to the original one, without noticeably sacrificing the quality in terms of classification accuracy. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy.

Connected through hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite"):
  with members:
    - "mlperf training benchmark"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "distributional semantic modeling a revised technique to train term word vector space models applying the ontology related approach"):
  with members:
    - "development of formal models algorithms procedures engineering and functioning of the software system instrumental complex for ontological engineering purpose"
    - "deep contextualized word representations"
    - "integrated tools for engineering ontologies"
    - "to the problem of the instrumental complex for ontological engineering purpose software system design"
    - "research and development workstation environment the new class of current research information systems"
    - "word2vec applied to recommendation hyperparameters matter"
    - "technique for designing a domain ontology"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "generation distillation for efficient natural language understanding in low data settings"):
  with members:
    - "distilling task specific knowledge from bert into simple neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "decoupled weight decay regularization"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"

Connected through hyperedge HE-4 (induced by source paper "shared task lexical semantic change detection in german"):
  with members:
    - "time out temporal referencing for robust modeling of lexical semantic change"
    - "a wind of change detecting and evaluating lexical semantic change across times and domains"
    - "survey of computational approaches to diachronic conceptual change"
    - "word translation without parallel data"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "sparse overcomplete word vector representations"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"

Connected through hyperedge HE-5 (induced by source paper "text classification with word embedding regularization and soft similarity measure"):
  with members:
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "compressing word embeddings via deep compositional code learning"
    - "compressing neural networks with the hashing trick"
    - "a comparative study on regularization strategies for embedding based neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning deep transformer models for machine translation"
    - "pre trained language model representations for language generation"

Connected through hyperedge HE-7 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "tensorized embedding layers for efficient model compression"):
  with members:
    - "a tensorized transformer for language modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "generalized tensor models for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"

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)