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: "relevancy classification of multimodal social media streams for emergency services"
  Abstract: Social media has become an integral part of our daily lives. During time-critical events, the public shares a variety of posts on social media including reports for resource needs, damages, and help offerings for the affected community. Such posts can be relevant and may contain valuable situational awareness information. However, the information overload of social media challenges the timely processing and extraction of relevant information by the emergency services. Furthermore, the growing usage of multimedia content in the social media posts in recent years further adds to the challenge in timely mining relevant information from social media. In this paper, we present a novel method for multimodal relevancy classification of social media posts, where relevancy is defined with respect to the information needs of emergency management agencies. Specifically, we experiment with the combination of semantic textual features with the image features to efficiently classify a relevant multimodal social media post. We validate our method using an evaluation of classifying the data from three real-world crisis events. Our experiments demonstrate that features based on the proposed hybrid framework of exploiting both textual and image content improve the performance of identifying relevant posts. In the light of these experiments, the application of the proposed classification method could reduce cognitive load on emergency services, in filtering multimodal public posts at large scale.

Connected through hyperedge HE-1 (induced by source paper "classifying relevant social media posts during disasters using ensemble of domain agnostic and domain specific word embeddings"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "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"

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)