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 "necessary and sufficient budgets in information source finding with querying adaptivity gap"):
  Abstract: In this paper, we study a problem of detecting the source of diffused information by querying individuals, given a sample snapshot of the information diffusion graph, where two queries are asked: {\em (i)} whether the respondent is the source or not, and {\em (ii)} if not, which neighbor spreads the information to the respondent. We consider the case when respondents may not always be truthful and some cost is taken for each query. Our goal is to quantify the necessary and sufficient budgets to achieve the detection probability $1-\delta$ for any given $0<\delta<1.$ To this end, we study two types of algorithms: adaptive and non-adaptive ones, each of which corresponds to whether we adaptively select the next respondents based on the answers of the previous respondents or not. We first provide the information theoretic lower bounds for the necessary budgets in both algorithm types. In terms of the sufficient budgets, we propose two practical estimation algorithms, each of non-adaptive and adaptive types, and for each algorithm, we quantitatively analyze the budget which ensures $1-\delta$ detection accuracy. This theoretical analysis not only quantifies the budgets needed by practical estimation algorithms achieving a given target detection accuracy in finding the diffusion source, but also enables us to quantitatively characterize the amount of extra budget required in non-adaptive type of estimation, refereed to as {\em adaptivity gap}. We validate our theoretical findings over synthetic and real-world social network topologies.

Includes members:
  Member "rumor source detection under querying with untruthful answers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  Member "back to the past source identification in diffusion networks from partially observed cascades":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade size distributions and why they matter")
  Member "locally adaptive optimization adaptive seeding for monotone submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient approximation algorithms for adaptive seed minimization")
  Member "a robust information source estimator with sparse observations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information source detection with limited time knowledge")
  Member "rooting out the rumor culprit from suspects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "network topology inference using information cascades with limited statistical knowledge")
  Member "finding an infection source under the sis model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gfcn a new graph convolutional network based on parallel flows")
  Member "information source detection in the sir model a sample path based approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media")

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)