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: "processing social media messages in mass emergency a survey"
  Abstract: Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: handling information overload, filtering credible information, and prioritizing different classes of messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages, focusing on their application in emergency response scenarios. We examine the particularities of this setting, and then methodically examine a series of key sub-problems ranging from the detection of events to the creation of actionable and useful summaries.

Connected through hyperedge HE-1 (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-2 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-3 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach"):
  with members:
    - "a survey of location prediction on twitter"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "multiview deep learning for predicting twitter users location"
    - "a probabilistic framework for location inference from social media"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "improving short text classification through global augmentation methods"):
  with members:
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "data augmentation for low resource neural machine translation"
    - "automated hate speech detection and the problem of offensive language"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "keyphrase extraction from disaster related tweets"):
  with members:
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "dissecting contextual word embeddings architecture and representation"
    - "deep keyphrase generation"
    - "semi supervised discovery of informative tweets during the emerging disasters"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "recurrent neural network for text classification with multi task learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-6 (induced by source paper "unsupervised detection of sub events in large scale disasters"):
  with members:
    - "sub event detection from twitter streams as a sequence labeling problem"
    - "graph based semi supervised learning with convolution neural networks to classify crisis related tweets"
    - "empathi an ontology for emergency managing and planning about hazard crisis"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "domain adaptation with adversarial training and graph embeddings"
    - "fasttext zip compressing text classification models"
    - "intelligent disaster response via social media analysis a survey"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"

Connected through hyperedge HE-7 (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"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "characterizing collective attention via descriptor context a case study of public discussions of crisis events"):
  with members:
    - "pomo generating entity specific post modifiers in context"
    - "massively multilingual transfer for ner"
    - "the effect of collective attention on controversial debates on social media"
    - "credibility and dynamics of collective attention"
    - "globally normalized transition based neural networks"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "dynamical classes of collective attention in twitter"
    - "novelty and collective attention"

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.SI(Social and Information Networks)