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: "rapid classification of crisis related data on social networks using convolutional neural networks"
  Abstract: The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for binary and multi-class tweet classification task. We show that neural network based models do not require any feature engineering and perform better than state-of-the-art methods. In the early hours of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results.

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"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "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 "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"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-3 (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"
    - "sub story detection in twitter with hierarchical dirichlet processes"

Connected through hyperedge HE-4 (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"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "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-5 (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"
    - "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-6 (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"
    - "applications of online deep learning for crisis response using social media information"
    - "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-7 (induced by source paper "deep learning and word embeddings for tweet classification for crisis response"):
  with members:
    - "applications of online deep learning for crisis response using social media information"
    - "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 "mining social media for newsgathering a review"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "fake news detection on social media a data mining perspective"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "hoaxy a platform for tracking online misinformation"
    - "computational fact checking from knowledge networks"

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)