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: "finding eyewitness tweets during crises"
  Abstract: Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time?

Connected through hyperedge HE-1 (induced by source paper "understanding the radical mind identifying signals to detect extremist content on twitter"):
  with members:
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (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"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "intelligent disaster response via social media analysis a survey"):
  with members:
    - "quotus the structure of political media coverage as revealed by quoting patterns"
    - "processing social media messages in mass emergency a survey"
    - "a survey of prediction using social media"
    - "dynamical classes of collective attention in twitter"
    - "truthy mapping the spread of astroturf in microblog streams"
    - "predicting the popularity of online content"

Connected through hyperedge HE-4 (induced by source paper "filtering tweets for social unrest"):
  with members:
    - "analysis of stopping active learning based on stabilizing predictions"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-5 (induced by source paper "mining social media to inform peatland fire and haze disaster management"):
  with members:
    - "attribution of responsibility and blame regarding a man made disaster flintwatercrisis"
    - "processing social media messages in mass emergency a survey"

Connected through hyperedge HE-6 (induced by source paper "breaking disasters predicting and characterizing the global news value of natural and man made disasters"):
  with members:
    - "understanding news geography and major determinants of global news coverage of disasters"

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)