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 "using social media to predict the future a systematic literature review"):
  Abstract: Social media (SM) data provides a vast record of humanity's everyday thoughts, feelings, and actions at a resolution previously unimaginable. Because user behavior on SM is a reflection of events in the real world, researchers have realized they can use SM in order to forecast, making predictions about the future. The advantage of SM data is its relative ease of acquisition, large quantity, and ability to capture socially relevant information, which may be difficult to gather from other data sources. Promising results exist across a wide variety of domains, but one will find little consensus regarding best practices in either methodology or evaluation. In this systematic review, we examine relevant literature over the past decade, tabulate mixed results across a number of scientific disciplines, and identify common pitfalls and best practices. We find that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks. But despite these shortcomings, recurring findings and promising results continue to galvanize researchers and demand continued investigation. Based on the existing literature, we identify research practices which lead to success, citing specific examples in each case and making recommendations for best practices. These recommendations will help researchers take advantage of the exciting possibilities offered by SM platforms.

Includes members:
  Member "what the language you tweet says about your occupation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social media based user embedding a literature review")
  Member "mining online social data for detecting social network mental disorders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient algorithms towards network intervention")
  Member "sentiment aggregate functions for political opinion polling using microblog streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on computational politics")
  Member "using user generated online photos to estimate and monitor air pollution in major cities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maplur exploring a new paradigm for estimating air pollution using deep learning on map images")
  Member "beating the news with embers forecasting civil unrest using open source indicators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "titan a spatiotemporal feature learning framework for traffic incident duration prediction")
  Member "predicting crowd behavior with big public data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "big questions for social media big data representativeness validity and other methodological pitfalls":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "geotagging one hundred million twitter accounts with total variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "demographic inference and representative population estimates from multilingual social media data")

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.CY(Computers and Society)