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: "choosing to grow a graph modeling network formation as discrete choice"
  Abstract: We provide a framework for modeling social network formation through conditional multinomial logit models from discrete choice and random utility theory, in which each new edge is viewed as a "choice" made by a node to connect to another node, based on (generic) features of the other nodes available to make a connection. This perspective on network formation unifies existing models such as preferential attachment, triadic closure, and node fitness, which are all special cases, and thereby provides a flexible means for conceptualizing, estimating, and comparing models. The lens of discrete choice theory also provides several new tools for analyzing social network formation; for example, the significance of node features can be evaluated in a statistically rigorous manner, and mixtures of existing models can be estimated by adapting known expectation-maximization algorithms. We demonstrate the flexibility of our framework through examples that analyze a number of synthetic and real-world datasets. For example, we provide rigorous methods for estimating preferential attachment models and show how to separate the effects of preferential attachment and triadic closure. Non-parametric estimates of the importance of degree show a highly linear trend, and we expose the importance of looking carefully at nodes with degree zero. Examining the formation of a large citation graph, we find evidence for an increased role of degree when accounting for age.

Connected through hyperedge HE-1 (induced by source paper "choice set optimization under discrete choice models of group decisions"):
  with members:
    - "learning choice functions"
    - "learning mixtures of plackett luce models from structured partial orders"
    - "discovering context effects from raw choice data"
    - "on the convergence of adam and beyond"
    - "learning choice functions concepts and architectures"
    - "pytorch an imperative style high performance deep learning library"
    - "predicting choice with set dependent aggregation"
    - "reasoning in bayesian opinion exchange networks is pspace hard"

Connected through hyperedge HE-2 (induced by source paper "modeling and analysis of tagging networks in stack exchange communities"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "who is authoritative understanding reputation mechanisms in quora"
    - "discovering social circles in ego networks"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "collective dynamics of social annotation"

Connected through hyperedge HE-3 (induced by source paper "network interpolation"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "a sampling framework for counting temporal motifs"
    - "motifs in temporal networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "dynamic stochastic blockmodels for time evolving social networks"

Connected through hyperedge HE-4 (induced by source paper "predicting choice with set dependent aggregation"):
  with members:
    - "learning choice functions"
    - "discovering context effects from raw choice data"
    - "choosing to rank"
    - "comparison based choices"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-5 (induced by source paper "changing the tune mixtures of network models that vary in time"):
  with members:
    - "multiscale mixing patterns in networks"
    - "motifs in temporal networks"
    - "detecting change points in the large scale structure of evolving 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)