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: "word2bits quantized word vectors"
  Abstract: Word vectors require significant amounts of memory and storage, posing issues to resource limited devices like mobile phones and GPUs. We show that high quality quantized word vectors using 1-2 bits per parameter can be learned by introducing a quantization function into Word2Vec. We furthermore show that training with the quantization function acts as a regularizer. We train word vectors on English Wikipedia (2017) and evaluate them on standard word similarity and analogy tasks and on question answering (SQuAD). Our quantized word vectors not only take 8-16x less space than full precision (32 bit) word vectors but also outperform them on word similarity tasks and question answering.

Connected through hyperedge HE-1 (induced by source paper "quantized neural network inference with precision batching"):
  with members:
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "quantized reinforcement learning quarl"
    - "adaptivfloat a floating point based data type for resilient deep learning inference"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "alternating multi bit quantization for recurrent neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-2 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-3 (induced by source paper "hamming sentence embeddings for information retrieval"):
  with members:
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "compressing word embeddings via deep compositional code learning"
    - "enriching word vectors with subword information"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-4 (induced by source paper "binarized canonical polyadic decomposition for knowledge graph completion"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
    - "a survey on methods and theories of quantized neural networks"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "on multi relational link prediction with bilinear models"
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-5 (induced by source paper "binarized knowledge graph embeddings"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
    - "a survey on methods and theories of quantized neural networks"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "cosine compressive network embedding on large scale information networks"):
  with members:
    - "mile a multi level framework for scalable graph embedding"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "compressing word embeddings via deep compositional code learning"
    - "natural language processing with small feed forward networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "groupreduce block wise low rank approximation for neural language model shrinking"):
  with members:
    - "deep neural network compression with single and multiple level quantization"
    - "opennmt open source toolkit for neural machine translation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compressing recurrent neural network with tensor train"
    - "exploring sparsity in recurrent neural networks"
    - "compressing word embeddings via deep compositional code learning"
    - "learning compact recurrent neural networks"
    - "exploring the limits of language modeling"

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)