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: "how random are online social interactions"
  Abstract: The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online makes it appear as if these studies cannot reveal predictive social behaviors that could be used for developing better platforms and services. We use two large social databases to measure the mutual information entropy that both individual and group actions generate as they evolve over time. We show that user's interaction sequences have strong deterministic components, in contrast with existing assumptions and models. In addition, we show that individual interactions are more predictable when users act on their own rather than when attending group activities.

Connected through hyperedge HE-1 (induced by source paper "entropy rate estimation for markov chains with large state space"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "data noising as smoothing in neural network language models"
    - "factorization tricks for lstm networks"
    - "neural architecture search with reinforcement learning"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "temporal pattern of online communication spike trains in spreading a scientific rumor how often who interacts with whom"):
  with members:
    - "the majority illusion in social networks"
    - "local variation of hashtag spike trains and popularity in twitter"
    - "local variation of collective attention in hashtag spike trains"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "online popularity and topical interests through the lens of instagram"
    - "modelling of trends in twitter using retweet graph dynamics"
    - "the bursty dynamics of the twitter information network"
    - "attention competition with advertisement"

Connected through hyperedge HE-3 (induced by source paper "detecting memory and structure in human navigation patterns using markov chain models of varying order"):
  with members:
    - "evaluating variable length markov chain models for analysis of user web navigation sessions"
    - "novelty and social search in the world wide web"

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.CY(Computers and Society)