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: "online embedding compression for text classification using low rank matrix factorization"
  Abstract: Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training,to compress the word embedding layer which represents the size bottleneck for most NLP models. Our models are trained, compressed and then further re-trained on the downstream task to recover accuracy while maintaining the reduced size. Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression. We also analyze the inference time and storage space for our method through FLOP calculations, showing that we can compress DNN models by a configurable ratio and regain accuracy loss without introducing additional latency compared to fixed point quantization. Finally, we introduce a novel learning rate schedule, the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate to outperform other popular adaptive learning rate algorithms on a sentence classification benchmark.

Connected through hyperedge HE-1 (induced by source paper "define deep factorized input word embeddings for neural sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "adaptive input representations for neural language modeling"
    - "pyramidal recurrent unit for language modeling"

Connected through hyperedge HE-2 (induced by source paper "compression of recurrent neural networks for efficient language modeling"):
  with members:
    - "trellis networks for sequence modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian compression for natural language processing"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"
    - "pointing the unknown words"

Connected through hyperedge HE-3 (induced by source paper "distilled embedding non linear embedding factorization using knowledge distillation"):
  with members:
    - "tensorized embedding layers for efficient model compression"
    - "multilingual neural machine translation with knowledge distillation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "deep self representative concept factorization network for representation learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "the trustworthy pal controlling the false discovery rate in boolean matrix factorization"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "did distributed incremental block coordinate descent for nonnegative matrix factorization"

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)