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: "multichannel variable size convolution for sentence classification"
  Abstract: We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and largescale Twitter sentiment prediction and on subjectivity classification.

Connected through hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "sgm sequence generation model for multi label classification"
    - "graph convolutional networks for text classification"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "capsule networks against medical imaging data challenges"
    - "dynamic routing between capsules"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-4 (induced by source paper "using similarity measures to select pretraining data for ner"):
  with members:
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "what s in a domain learning domain robust text representations using adversarial training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-5 (induced by source paper "semantic relatedness based re ranker for text spotting"):
  with members:
    - "universal sentence encoder"
    - "visual re ranking with natural language understanding for text spotting"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "visual attention models for scene text recognition"
    - "a decomposable attention model for natural language inference"
    - "breakingnews article annotation by image and text processing"
    - "bag of tricks for efficient text classification"
    - "coco text dataset and benchmark for text detection and recognition in natural images"

Connected through hyperedge HE-6 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "vital a visual interpretation on text with adversarial learning for image labeling"):
  with members:
    - "unsupervised textual grounding linking words to image concepts"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "conditional image text embedding networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "learning what and where to draw"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-8 (induced by source paper "self balanced dropout"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "on the inductive bias of dropout"
    - "convolutional neural networks for sentence classification"

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)