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: "information transfer in social media"
  Abstract: Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information-theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine-grain notions of influence, and admits a natural, predictive interpretation. Causal networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.

Connected through hyperedge HE-1 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers"):
  with members:
    - "deepinf social influence prediction with deep learning"
    - "learning hawkes processes from short doubly censored event sequences"
    - "the social dynamics of language change in online networks"
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "learning influence functions from incomplete observations"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "adversarial orthogonal regression two non linear regressions for causal inference"):
  with members:
    - "mine mutual information neural estimation"
    - "k nn estimation of directed information"
    - "adam a method for stochastic optimization"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "directed information graphs"
    - "information theoretic measures of influence based on content dynamics"

Connected through hyperedge HE-3 (induced by source paper "a cross repository model for predicting popularity in github"):
  with members:
    - "a communication protocol for man machine networks"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "computational landscape of user behavior on social media"):
  with members:
    - "computational complexity of the minimum state probabilistic finite state learning problem on finite data sets"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "analysis of twitter traffic based on renewal densities"
    - "enumerating finitary processes"

Connected through hyperedge HE-5 (induced by source paper "wearable affective life log system for understanding emotion dynamics in daily life"):
  with members:
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"

Connected through hyperedge HE-6 (induced by source paper "on the influence of social bots in online protests"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"
    - "vaporous marketing uncovering pervasive electronic cigarette advertisements on twitter"
    - "the new war correspondents the rise of civic media curation in urban warfare"
    - "a meta analysis of state of the art electoral prediction from twitter data"

Connected through hyperedge HE-7 (induced by source paper "the darpa twitter bot challenge"):
  with members:
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "tripartite graph clustering for dynamic sentiment analysis on social media"
    - "entropy based classification of retweeting activity on twitter"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-8 (induced by source paper "evolution of privacy loss in wikipedia"):
  with members:
    - "link prediction by de anonymization how we won the kaggle social network challenge"
    - "echoes of power language effects and power differences in social interaction"

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)