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: "crisismmd multimodal twitter datasets from natural disasters"
  Abstract: During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.

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"
    - "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"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"

Connected through hyperedge HE-2 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "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"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "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-3 (induced by source paper "natural disasters detection in social media and satellite imagery a survey"):
  with members:
    - "disaster monitoring using unmanned aerial vehicles and deep learning"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "deep cross residual learning for multitask visual recognition"
    - "enriching word vectors with subword information"
    - "large scale image retrieval with attentive deep local features"
    - "nazr cnn object detection and fine grained classification in crowdsourced uav images"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "low supervision urgency detection and transfer in short crisis messages"):
  with members:
    - "domain adaptation with adversarial training and graph embeddings"
    - "information extraction in illicit web domains"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "bag of tricks for efficient text classification"
    - "document embedding with paragraph vectors"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "relevancy classification of multimodal social media streams for emergency services"):
  with members:
    - "a twitter tale of three hurricanes harvey irma and maria"
    - "xgboost a scalable tree boosting system"
    - "deep residual learning for image recognition"
    - "processing social media messages in mass emergency a survey"

Connected through hyperedge HE-7 (induced by source paper "modeling human annotation errors to design bias aware systems for social stream processing"):
  with members:
    - "quantifying the impact of cognitive biases in question answering systems"
    - "ranking of social media alerts with workload bounds in emergency operation centers"
    - "engineering crowdsourced stream processing systems"

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)