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: "a practitioners guide to transfer learning for text classification using convolutional neural networks"
  Abstract: Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of image-based applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.

Connected through hyperedge HE-1 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system"):
  with members:
    - "a survey of location prediction on twitter"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "processing social media messages in mass emergency a survey"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-2 (induced by source paper "combination of multiple deep learning architectures for offensive language detection in tweets"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "enriching word vectors with subword information"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep transfer reinforcement learning for text summarization"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "adversarial reprogramming of text classification neural networks"):
  with members:
    - "adversarial patch"
    - "adversarial reprogramming of neural networks"
    - "greedy attack and gumbel attack generating adversarial examples for discrete data"
    - "black box attacks against rnn based malware detection algorithms"
    - "neural machine translation with gumbel greedy decoding"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (induced by source paper "sieve helping developers sift wheat from chaff via cross platform analysis"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "sentiment polarity detection for software development"
    - "semantically enhanced software traceability using deep learning techniques"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "transfer learning for named entity recognition with neural networks"
    - "enriching word vectors with subword information"
    - "how transferable are neural networks in nlp applications"
    - "an insight into the unresolved questions at stack overflow"

Connected through hyperedge HE-6 (induced by source paper "on generality and knowledge transferability in cross domain duplicate question detection for heterogeneous community question answering"):
  with members:
    - "bilateral multi perspective matching for natural language sentences"
    - "xgboost a scalable tree boosting system"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "how transferable are neural networks in nlp applications"

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)