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: "combining traditional marketing and viral marketing with amphibious influence maximization"
  Abstract: In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. #R##N#We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-\epsilon)^3$ for any (polynomially small) $\epsilon > 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.

Connected through hyperedge HE-1 (induced by source paper "maximizing welfare in social networks under a utility driven influence diffusion model"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "inferring networks of substitutable and complementary products"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-2 (induced by source paper "scalable bicriteria algorithms for the threshold activation problem in online social networks"):
  with members:
    - "least cost influence maximization across multiple social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "information diffusion and external influence in networks"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

Connected through hyperedge HE-3 (induced by source paper "almost polynomial ratio eth hardness of approximating densest k subgraph"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "eth hardness for signaling in symmetric zero sum games"
    - "approximating dense max 2 csps"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"

Connected through hyperedge HE-4 (induced by source paper "boosting information spread an algorithmic approach"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "the simple rules of social contagion"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing acceptance probability for active friending in on line social networks"
    - "social dynamics of digg"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "cumulative activation in social networks"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "shaping social activity by incentivizing users"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "minimizing seed set selection with probabilistic coverage guarantee in a social network"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-6 (induced by source paper "eth hardness for densest k subgraph with perfect completeness"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "sum of squares lower bounds for planted clique"
    - "am with multiple merlins"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "finding endogenously formed communities"
    - "finding overlapping communities in social networks toward a rigorous approach"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"

Connected through hyperedge HE-7 (induced by source paper "locally adaptive optimization adaptive seeding for monotone submodular functions"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "maximizing acceptance probability for active friending in on line social networks"
    - "maximizing social influence in nearly optimal time"
    - "symmetry and approximability of submodular maximization problems"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "inferring networks of diffusion and influence"
    - "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)