human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised detection of sub events in large scale disasters"):
  Abstract: Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency ``event'', such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

Includes members:
  Member "sub event detection from twitter streams as a sequence labeling problem":
    also connected through other hyperedges:
      (none)
  Member "graph based semi supervised learning with convolution neural networks to classify crisis related tweets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "newswire versus social media for disaster response and recovery")
  Member "empathi an ontology for emergency managing and planning about hazard crisis":
    also connected through other hyperedges:
      (none)
  Member "savitr a system for real time location extraction from microblogs during emergencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")
  Member "domain adaptation with adversarial training and graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services")
  Member "fasttext zip compressing text classification models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "intelligent disaster response via social media analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forecasting people s needs in hurricane events from social network")
  Member "rapid classification of crisis related data on social networks using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services")

Question: Please tell me which class the source paper 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)