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: "applications of online deep learning for crisis response using social media information"
  Abstract: During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. We test our models using a crisis-related real-world Twitter dataset.

Connected through hyperedge HE-1 (induced by source paper "attention based neural architecture for rumor detection with author context awareness"):
  with members:
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "call attention to rumors deep attention based recurrent neural networks for early rumor detection"
    - "quantifying mental health from social media with neural user embeddings"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "csi a hybrid deep model for fake news detection"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "stance detection with bidirectional conditional encoding"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "processing social media messages in mass emergency a survey"

Connected through hyperedge HE-3 (induced by source paper "interactive learning for identifying relevant tweets to support real time situational awareness"):
  with members:
    - "on identifying disaster related tweets matching based or learning based"
    - "comparative study of cnn and rnn for natural language processing"
    - "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"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "deep learning and word embeddings for tweet classification for crisis response"):
  with members:
    - "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"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "automatic identification and ranking of emergency aids in social media macro community"):
  with members:
    - "summarizing situational and topical information during crises"
    - "visual themes and sentiment on social networks to aid first responders during crisis events"

Connected through hyperedge HE-6 (induced by source paper "on identifying disaster related tweets matching based or learning based"):
  with members:
    - "semi supervised discovery of informative tweets during the emerging disasters"
    - "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
    - "distributed representations of sentences and documents"
    - "processing social media messages in mass emergency a survey"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "distributed representations of words and phrases and their compositionality"
    - "the new war correspondents the rise of civic media curation in urban warfare"

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)