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: "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
  Abstract: Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.

Connected through hyperedge HE-1 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services"):
  with members:
    - "diversity based generalization for neural unsupervised text classification under domain shift"
    - "domain adaptation with adversarial training and graph embeddings"
    - "advances in pre training distributed word representations"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "effective approaches to attention based neural machine translation"
    - "learning multiple tasks with deep relationship networks"
    - "gated feedback recurrent neural networks"
    - "learning multiple tasks with multilinear relationship networks"

Connected through hyperedge HE-2 (induced by source paper "localized flood detectionwith minimal labeled social media data using transfer learning"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "domain adaptation with adversarial training and graph embeddings"
    - "recurrent neural network for text classification with multi task learning"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-4 (induced by source paper "uneven coverage of natural disasters in wikipedia the case of flood"):
  with members:
    - "integrating social media into a pan european flood awareness system a multilingual approach"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "not at home on the range peer production and the urban rural divide"
    - "disaster monitoring with wikipedia and online social networking sites structured data and linked data fragments to the rescue"

Connected through hyperedge HE-5 (induced by source paper "few shot tweet detection in emerging disaster events"):
  with members:
    - "infinite mixture prototypes for few shot learning"
    - "social media usage patterns during natural hazards"
    - "domain adaptation with adversarial training and graph embeddings"
    - "prototypical networks for few shot learning"
    - "applications of online deep learning for crisis response using social media information"
    - "matching networks for one shot learning"
    - "convolutional neural networks for sentence classification"
    - "processing social media messages in mass emergency a survey"

Connected through hyperedge HE-6 (induced by source paper "a deep learning approach for tweet classification and rescue scheduling for effective disaster management"):
  with members:
    - "coordinating disaster emergency response with heuristic reinforcement learning"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "very deep convolutional networks for text classification"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "effective approaches to attention based neural machine translation"
    - "learning natural language inference with lstm"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "a survey of location prediction on twitter"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "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-8 (induced by source paper "classifying relevant social media posts during disasters using ensemble of domain agnostic and domain specific word embeddings"):
  with members:
    - "relevancy classification of multimodal social media streams for emergency services"
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "using word embeddings in twitter election classification"
    - "enriching word vectors with subword information"
    - "processing social media messages in mass emergency a survey"
    - "distributed representations of words and phrases and their compositionality"

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)