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 "a framework of algorithms computing the bias and prestige of nodes in trust networks"):
  Abstract: A trust network is a social network in which edges represent the trust relationship between two nodes in the network. In a trust network, a fundamental question is how to assess and compute the bias and prestige of the nodes, where the bias of a node measures the trustworthiness of a node and the prestige of a node measures the importance of the node. The larger bias of a node implies the lower trustworthiness of the node, and the larger prestige of a node implies the higher importance of the node. In this paper, we define a vector-valued contractive function to characterize the bias vector which results in a rich family of bias measurements, and we propose a framework of algorithms for computing the bias and prestige of nodes in trust networks. Based on our framework, we develop four algorithms that can calculate the bias and prestige of nodes effectively and robustly. The time and space complexities of all our algorithms are linear with respect to the size of the graph, thus our algorithms are scalable to handle large datasets. We evaluate our algorithms using five real datasets. The experimental results demonstrate the effectiveness, robustness, and scalability of our algorithms.

Includes members:
  Member "a robust ranking algorithm to spamming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accumulative time based ranking method to reputation evaluation in information networks")
  Member "the slashdot zoo mining a social network with negative edges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep network embedding for graph representation learning in signed networks")

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)