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 diffusion in social networks in two phases"
  Abstract: The problem of maximizing information diffusion, given a certain budget expressed in terms of the number of seed nodes, is an important topic in social networks research. Existing literature focuses on single phase diffusion where all seed nodes are selected at the beginning of diffusion and all the selected nodes are activated simultaneously. This paper undertakes a detailed investigation of the effect of selecting and activating seed nodes in multiple phases. Specifically, we study diffusion in two phases assuming the well-studied independent cascade model. First, we formulate an objective function for two-phase diffusion, investigate its properties, and propose efficient algorithms for finding seed nodes in the two phases. Next, we study two associated problems: (1)  budget splitting  which seeks to optimally split the total budget between the two phases and (2)  scheduling  which seeks to determine an optimal delay after which to commence the second phase. Our main conclusions include: (a) under strict temporal constraints, use single phase diffusion, (b) under moderate temporal constraints, use two-phase diffusion with a short delay while allocating most of the budget to the first phase, and (c) when there are no temporal constraints, use two-phase diffusion with a long delay while allocating roughly one-third of the budget to the first phase.

Connected through hyperedge HE-1 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks"):
  with members:
    - "coupled deep learning for heterogeneous face recognition"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "revisiting semi supervised learning with graph embeddings"
    - "spreading processes in multilayer networks"
    - "a survey of heterogeneous information network analysis"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "a two phase investment game for competitive opinion dynamics in social networks"):
  with members:
    - "an integrated framework for competitive multi channel marketing of multi featured products"
    - "multi round influence maximization"
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "no time to observe adaptive influence maximization with partial feedback"

Connected through hyperedge HE-3 (induced by source paper "optimal multiphase investment strategies for influencing opinions in a social network"):
  with members:
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "adaptive influence maximization in dynamic social networks"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "strategic disclosure of opinions on a social network"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "a multi phase approach for improving information diffusion in social networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

Connected through hyperedge HE-4 (induced by source paper "a survey on influence maximization in a social network"):
  with members:
    - "modeling information diffusion in social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "on budgeted influence maximization in social networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-5 (induced by source paper "effectiveness of diffusing information through a social network in multiple phases"):
  with members:
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "scalable methods for adaptively seeding a social network"
    - "irie scalable and robust influence maximization in social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)