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: "approximation analysis of influence spread in social networks"
  Abstract: In the context of influence propagation in a social graph, we can identify three orthogonal dimensions - the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagation. We can constrain one or two of these and try to optimize the third. In their seminal paper (12), Kempe, Kleinberg and Tardos (KKT) constrained the budget, left time unconstrained, and maximized the coverage. In this paper, we study alternative optimization problems: MINSEED and MINTIME. In MINSEED, a coverage thresholdis given and the task is to find the minimum size seed set such that by activating it, at leastnodes are eventually activated in the expected sense. In MINTIME, a coverage thresholdand a budget threshold k are given, and the task is to find a seed set of size at most k such that by activating it, at leastnodes are activated in the expected sense, in the minimum possible time. It turns out both these problems are NP-hard. Given the hardness of the problems, we naturally turn to the subject of approximation algo- rithms. For MINSEED, we develop a bicriteria approximation by exploiting its relationship to the problem of Real-valued Submodular Set Cover (RSSC). We prove a generic inapproximabil- ity result for RSSC suggesting that improving this approximation factor is likely to be hard. For MINTIME we show that even bicriteria and tricriteria approximations are hard under several conditions. Our proof exploits the relationship between MINTIME and the problem of Robust Asymmetric k-center (RAKC). We show, however, that if we allow the budget for number of seeds k to be boosted by a logarithmic factor and allow the coverage to fall short, then the problem can be solved exactly in PTIME, i.e., we can achieve the required coverage within the time achieved by the optimal solution to MINTIME with budget k and coverage threshold �. Finally, we show the value of the approximation algorithms, by conducting an experimental comparison of their quality against that achieved by various heuristics.

Connected through hyperedge HE-1 (induced by source paper "coevolve a joint point process model for information diffusion and network co evolution"):
  with members:
    - "co evolutionary dynamics in social networks a case study of twitter"
    - "modeling adoption and usage of competing products"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "the role of information diffusion in the evolution of social networks"
    - "modeling information propagation with survival theory"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "social dynamics of digg"

Connected through hyperedge HE-2 (induced by source paper "precoder design for physical layer multicasting"):
  with members:
    - "efficient submodular function maximization under linear packing constraints"
    - "ranking with submodular valuations"
    - "on k column sparse packing programs"

Connected through hyperedge HE-3 (induced by source paper "influence maximization in continuous time diffusion networks"):
  with members:
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-4 (induced by source paper "on budgeted influence maximization in social networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-5 (induced by source paper "on the non progressive spread of influence through social networks"):
  with members:
    - "on reversible cascades in scale free and erd h o s r enyi random graphs"
    - "on irreversible dynamic monopolies in general graphs"

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.DM(Discrete Mathematics)