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: "rooting out the rumor culprit from suspects"
  Abstract: Suppose that a rumor originating from a single source among a set of suspects spreads in a network, how to root out this rumor source? With the a priori knowledge of suspect nodes and a snapshot observation of infected nodes, we construct a maximum a posteriori (MAP) estimator to identify the rumor source using the susceptible-infected (SI) model. We propose to use a notion of local rumor center to characterize Pc(n), the correct detection probability of the source estimator upon observing n infected nodes, in both the finite and asymptotic regimes, for regular trees of node degree δ. First, when all nodes are suspects, limn→∞Pc(n) grows from 0.25 to 0.307 as δ increases from three to infinity, a result first established in Shah and Zaman (2011, 2012) via a different approach; furthermore, Pc(n) monotonically decreases with n and increases with δ even in the finite-n regime. Second, when the suspect nodes form a connected subgraph of the network, limn→∞Pc(n) significantly exceeds the a priori probability if δ ≥ 3, and reliable detection is achieved as δ becomes sufficiently large; furthermore, Pc(n) monotonically decreases with n and increases with δ. Third, when there are only two suspect nodes, limn→∞Pc(n) is at least 0.75 if δ ≥ 3; and Pc(n) increases with the distance between the two suspects. Fourth, when there are multiple suspect nodes, among all possible connection patterns, that all the suspects form a single connected subgraph yields the smallest Pc(n). Our analysis leverages ideas from the Polya's urn model in probability theory and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.

Connected through hyperedge HE-1 (induced by source paper "necessary and sufficient budgets in information source finding with querying adaptivity gap"):
  with members:
    - "rumor source detection under querying with untruthful answers"
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "a robust information source estimator with sparse observations"
    - "finding an infection source under the sis model"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-2 (induced by source paper "network topology inference using information cascades with limited statistical knowledge"):
  with members:
    - "estimating infection sources in networks using partial timestamps"
    - "back to the source an online approach for sensor placement and source localization"
    - "network topology inference from spectral templates"
    - "infection spreading and source identification a hide and seek game"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "learning laplacian matrix in smooth graph signal representations"
    - "how to identify an infection source with limited observations"

Connected through hyperedge HE-3 (induced by source paper "back to the source an online approach for sensor placement and source localization"):
  with members:
    - "observer placement for source localization the effect of budgets and transmission variance"
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "identification of source of rumors in social networks with incomplete information"
    - "approximability of the minimum weighted doubly resolving set problem"
    - "how to identify an infection source with limited observations"
    - "a robust information source estimator with sparse observations"
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-4 (induced by source paper "rumor source detection under querying with untruthful answers"):
  with members:
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "a robust information source estimator with sparse observations"
    - "finding an infection source under the sis model"
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-5 (induced by source paper "on the universality of jordan centers for estimating infection sources in tree networks"):
  with members:
    - "how to identify an infection source with limited observations"
    - "identifying infection sources and regions in large networks"
    - "a robust information source estimator with sparse observations"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-6 (induced by source paper "infection spreading and source identification a hide and seek game"):
  with members:
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "how to identify an infection source with limited observations"
    - "spy vs spy rumor source obfuscation"
    - "graphical evolutionary game for information diffusion over social networks"
    - "evolutionary dynamics of information diffusion over social networks"
    - "identifying infection sources and regions in large networks"

Connected through hyperedge HE-7 (induced by source paper "observer placement for source localization the effect of budgets and transmission variance"):
  with members:
    - "identification of source of rumors in social networks with incomplete information"
    - "the behavior of epidemics under bounded susceptibility"
    - "approximability of the minimum weighted doubly resolving set problem"
    - "how to identify an infection source with limited observations"
    - "spy vs spy rumor source obfuscation"
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-8 (induced by source paper "effector detection in social networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "information source detection in the sir model a sample path based approach"
    - "discovering social circles in ego networks"

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)