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: "scalable bicriteria algorithms for the threshold activation problem in online social networks"
  Abstract: We consider the Threshold Activation Problem (TAP): given social network $G$ and positive threshold $T$, find a minimum-size seed set $A$ that can trigger expected activation of at least $T$. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter $\alpha \in (0,1)$: given $\eta > 0$, with probability $1 - \eta$ our algorithm returns a solution $A$ with expected activation greater than $T - 2 \alpha T$, and the size of the solution $A$ is within factor $1 + 4 \alpha T + \log ( T )$ of the optimal size. The algorithm runs in time $O \left( \alpha^{-2}\log \left( n / \eta \right) (n + m) |A| \right)$, where $n$, $m$, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

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"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "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 "an efficient evolutionary algorithm for minimum cost submodular cover"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-3 (induced by source paper "multiplex influence maximization in online social networks with heterogeneous diffusion models"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "popular topics spread faster new dimension for influence propagation 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-4 (induced by source paper "toward optimal coupon allocation in social networks an approximate submodular optimization approach"):
  with members:
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "on k column sparse packing programs"

Connected through hyperedge HE-5 (induced by source paper "cost effective seed selection in online social networks"):
  with members:
    - "boosting information spread an algorithmic approach"
    - "least cost influence maximization across multiple 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"

Connected through hyperedge HE-6 (induced by source paper "popular topics spread faster new dimension for influence propagation in online social networks"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "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-7 (induced by source paper "vulnerability of clustering under node failure in complex networks"):
  with members:
    - "powers of tensors and fast matrix multiplication"

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)