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: "why approximate when you can get the exact optimal targeted viral marketing at scale"
  Abstract: One of the most central problems in viral marketing is Influence Maximization (IM), which finds a set of $k$ seed users who can influence the maximum number of users in online social networks. Unfortunately, all existing algorithms to IM, including the state of the art SSA and IMM, have an approximation ratio of $(1 - 1/e - \epsilon)$. Recently, a generalization of IM, Cost-aware Target Viral Marketing (CTVM), asks for the most cost-effective users to influence the most relevant users, has been introduced. The current best algorithm for CTVM has an approximation ratio of $(1 - 1 /\sqrt{e} - \epsilon)$. #R##N#In this paper, we study the CTVM problem, aiming to optimally solve the problem. We first highlight that using a traditional two stage stochastic programming to exactly solve CTVM is not possible because of scalability. We then propose an almost exact algorithm TipTop, which has an approximation ratio of $(1 - \epsilon)$. This result significantly improves the current best solutions to both IM and CTVM. At the heart of TipTop lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using Integer Programming. While obtaining an almost exact solution, TipTop is very scalable, running on billion-scale networks such as Twitter under three hours. Furthermore, TipTop lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.

Connected through hyperedge HE-1 (induced by source paper "submodular cost submodular cover with an approximate oracle"):
  with members:
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "cost effective seed selection in online social networks"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "submodular optimization under noise"
    - "an efficient streaming algorithm for the submodular cover problem"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-2 (induced by source paper "importance sample based approximation algorithm for cost aware targeted viral marketing"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "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"
    - "maximizing social influence in nearly optimal time"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "cost aware targeted viral marketing approximation with less samples"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "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"
    - "maximizing social influence in nearly optimal time"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "monotone submodular diversity functions for categorical vectors with application to diversification of seeds for targeted influence maximization"):
  with members:
    - "community interaction and conflict on the web"
    - "topology driven diversity for targeted influence maximization with application to user engagement in social networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-5 (induced by source paper "multiplex influence maximization in online social networks with heterogeneous diffusion models"):
  with members:
    - "popular topics spread faster new dimension for influence propagation in online social networks"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-6 (induced by source paper "tiptop exact solutions for influence maximization in billion scale networks"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-7 (induced by source paper "popular topics spread faster new dimension for influence propagation in online social networks"):
  with members:
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "minimizing seed set selection with probabilistic coverage guarantee in a social network"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "influence maximization in continuous time diffusion networks"

Connected through hyperedge HE-8 (induced by source paper "deterministic adaptive non submodular maximization via the primal curvature"):
  with members:
    - "string submodular functions with curvature constraints"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "near optimal bayesian active learning with noisy observations"

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)