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 "characterizing collective attention via descriptor context a case study of public discussions of crisis events"):
  Abstract: Collective attention, i.e. the public attention paid to a particular topic, is a key factor in understanding how emerging topics and breaking news spread in online discussions. In most research, collective attention on social media is measured via aggregate metrics, such as the number of posts that mention a given name. However, collective attention is expressed not only in frequency, but also in content: linguistic features of the description of events and names reflect how writers expect their readers to perceive the information being discussed. In this work, we conduct a large-scale language analysis of public online discussions of breaking news events on Facebook and Twitter, focusing on five recent crisis events. We examine how people refer to locations, focusing specifically on contextual descriptors, such as "San Juan" versus "San Juan, Puerto Rico." We find that the use of such descriptors is associated with proxies for social and informational expectations, including macro-level factors like the location's global importance and micro-level factors like audience engagement. We also find a consistent decrease in descriptor context use over time at a collective level, particularly for less active authors. These insights provide evidence for theories about information expectations in public discussions, and they inform how researchers and crisis response organizations can better understand public perception of crisis events as they unfold.

Includes members:
  Member "pomo generating entity specific post modifiers in context":
    also connected through other hyperedges:
      (none)
  Member "massively multilingual transfer for ner":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "the effect of collective attention on controversial debates on social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the role of political trolls in social media")
  Member "credibility and dynamics of collective attention":
    also connected through other hyperedges:
      (none)
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "rising tides or rising stars dynamics of shared attention on twitter during media events":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a model for meme popularity growth in social networking systems based on biological principle and human interest dynamics")
  Member "processing social media messages in mass emergency a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "localized flood detectionwith minimal labeled social media data using transfer learning")
  Member "dynamical classes of collective attention in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "language in our time an empirical analysis of hashtags")

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.CL(Computation and Language)