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: "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
  Abstract: During recent years the online social networks (in particular Twitter) have become an important alternative information channel to traditional media during natural disasters, but the amount and diversity of messages poses the challenge of information overload to end users. The goal of our research is to develop an automatic classifier of tweets to feed a mobile application that reduces the difficulties that citizens face to get relevant information during natural disasters. In this paper, we present in detail the process to build a classifier that filters tweets relevant and non-relevant to an earthquake. By using a dataset from the Chilean earthquake of 2010, we first build and validate a ground truth, and then we contribute by presenting in detail the effect of class imbalance and dimensionality reduction over 5 classifiers. We show how the performance of these models is affected by these variables, providing important considerations at the moment of building these systems.

Connected through hyperedge HE-1 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"):
  with members:
    - "what the language you tweet says about your occupation"
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "deep residual learning for image recognition"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "the digital life of walkable streets"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "applications of online deep learning for crisis response using social media information"
    - "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.SI(Social and Information Networks)