human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "optimizing node discovery on networks problem definitions fast algorithms and observations"):
  Abstract: Many people dream to become famous, YouTube video makers also wish their videos to have a large audience, and product retailers always hope to expose their products to customers as many as possible. Do these seemingly different phenomena share a common structure? We find that fame, popularity, or exposure, could be modeled as a node's discoverability on some properly defined network, and all of the previously mentioned phenomena can be commonly stated as a target node wants to be discovered easily by the other nodes in the network. In this work, we explicitly define a node's discoverability in a network, and formulate a general node discoverability optimization problem, where the goal is to create a budgeted set of incoming edges to the target node so as to optimize the target node's discoverability in the network. Although the optimization problem is proven to be NP-hard, we find that the defined discoverability measures have good properties that enable us to use a greedy algorithm to find provably near-optimal solutions. The computational complexity of a greedy algorithm is dominated by the time cost of an oracle call, i.e., calculating the marginal gain of a node. To scale up the oracle call over large networks, we propose an estimation-and-refinement approach, that provides a good trade-off between estimation accuracy and computational efficiency. Experiments conducted on real-world networks demonstrate that our method is thousands of times faster than an exact method using dynamic programming, thereby allowing us to solve the node discoverability optimization problem on large networks.

Includes members:
  Member "optimal tagging with markov chain optimization":
    also connected through other hyperedges:
      (none)
  Member "powerwalk scalable personalized pagerank via random walks with vertex centric decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed algorithms for fully personalized pagerank on large graphs")
  Member "scalable betweenness centrality maximization via sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach")
  Member "absorbing random walk centrality theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "current flow group closeness centrality for complex networks")
  Member "computing classic closeness centrality at scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning in network centrality measures tutorial and outlook")
  Member "axioms for centrality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximizing influence based group shapley centrality")
  Member "random walk domination in large graphs problem definitions and fast solutions":
    also connected through other hyperedges:
      (none)
  Member "using friends as sensors to detect global scale contagious outbreaks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")

Question: Please tell me which class the source paper 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)