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: "characterizing the demographics behind the blacklivesmatter movement"
  Abstract: The debates on minority issues are often dominated by or held among the concerned minority: gender equality debates have often failed to engage men, while those about race fail to effectively engage the dominant group. To test this observation, we study the #BlackLivesMatter}movement and hashtag on Twitter--which has emerged and gained traction after a series of events typically involving the death of African-Americans as a result of police brutality--and aim to quantify the population biases across user types (individuals vs. organizations), and (for individuals) across various demographics factors (race, gender and age). Our results suggest that more African-Americans engage with the hashtag, and that they are also more active than other demographic groups. We also discuss ethical caveats with broader implications for studies on sensitive topics (e.g. discrimination, mental health, or religion) that focus on users.

Connected through hyperedge HE-1 (induced by source paper "language in our time an empirical analysis of hashtags"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "hierarchical graph representation learning with differentiable pooling"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "is saki delicious the food perception gap on instagram and its relation to health"

Connected through hyperedge HE-2 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"

Connected through hyperedge HE-3 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "sampling from social networks with attributes"

Connected through hyperedge HE-4 (induced by source paper "protest activity detection and perceived violence estimation from social media images"):
  with members:
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "deep residual learning for image recognition"
    - "deep learning face attributes in the wild"
    - "6 seconds of sound and vision creativity in micro videos"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-5 (induced by source paper "divergent discourse between protests and counter protests blacklivesmatter and alllivesmatter"):
  with members:
    - "is language evolution grinding to a halt exploring the life and death of words in english fiction"
    - "predicting links in ego networks using temporal information"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "partisan asymmetries in online political activity"

Connected through hyperedge HE-6 (induced by source paper "greysanatomy vs yankees demographics and hashtag use on twitter"):
  with members:
    - "you are what apps you use demographic prediction based on user s apps"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "dynamical classes of collective attention in twitter"

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)