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: "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
  Abstract: Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (i) a max-pooling operation for improved interpretability; and (ii) a hierarchical pooling operation, which preserves spatial (n-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (i) (long) document classification; (ii) text sequence matching; and (iii) short text tasks, including classification and tagging. The source code and datasets can be obtained from https:// github.com/dinghanshen/SWEM.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "r transformer recurrent neural network enhanced transformer"
    - "learning to remember more with less memorization"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "efficient sentence embedding via semantic subspace analysis"):
  with members:
    - "sbert wk a sentence embedding method by dissecting bert based word models"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "post processing of word representations via variance normalization and dynamic embedding"

Connected through hyperedge HE-4 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model"):
  with members:
    - "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a large self annotated corpus for sarcasm"
    - "convolutional sequence to sequence learning"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-5 (induced by source paper "tensor graph convolutional networks for text classification"):
  with members:
    - "mr gnn multi resolution and dual graph neural network for predicting structured entity interactions"
    - "multi channel graph neural network for entity alignment"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "joint embedding of words and labels for text classification"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "leveraging clinical time series data for prediction a cautionary tale"
    - "universal language model fine tuning for text classification"
    - "scalable and accurate deep learning for electronic health records"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "improving palliative care with deep learning"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"

Connected through hyperedge HE-7 (induced by source paper "rest a thread embedding approach for identifying and classifying user specified information in security forums"):
  with members:
    - "ripex extracting malicious ip addresses from security forums using cross forum learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "early warnings of cyber threats in online discussions"
    - "relevance based word embedding"
    - "bag of tricks for efficient text classification"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "estimating training data influence by tracking gradient descent"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "representer point selection for explaining deep neural networks"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "deep residual learning for image recognition"
    - "sketching as a tool for numerical linear algebra"

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)