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: "the rise and fall of network stars analyzing 2 5 million graphs to reveal how high degree vertices emerge over time"
  Abstract: Trends change rapidly in today's world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that "shine." That is, at a specific time interval in a network's life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network's topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society.

Connected through hyperedge HE-1 (induced by source paper "the pushshift reddit dataset"):
  with members:
    - "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"
    - "towards ethical content based detection of online influence campaigns"
    - "content removal as a moderation strategy compliance and other outcomes in the changemyview community"
    - "are all successful communities alike characterizing and predicting the success of online communities"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "who let the trolls out towards understanding state sponsored trolls"

Connected through hyperedge HE-2 (induced by source paper "caveat emptor computational social science large scale missing data in a widely published reddit corpus"):
  with members:
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "a web of hate tackling hateful speech in online social spaces"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "analyzing complex network user arrival patterns and their effect on network topologies"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "what do vegans do in their spare time latent interest detection in multi community networks"

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)