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: "information source detection in the sir model a sample path based approach"
  Abstract: This paper studies the problem of detecting the information source in a network in which the spread of information follows the popular Susceptible-Infected-Recovered (SIR) model. We assume all nodes in the network are in the susceptible state initially except the information source which is in the infected state. Susceptible nodes may then be infected by infected nodes, and infected nodes may recover and will not be infected again after recovery. Given a snapshot of the network, from which we know all infected nodes but cannot distinguish susceptible nodes and recovered nodes, the problem is to find the information source based on the snapshot and the network topology. We develop a sample path based approach where the estimator of the information source is chosen to be the root node associated with the sample path that most likely leads to the observed snapshot. We prove for infinite-trees, the estimator is a node that minimizes the maximum distance to the infected nodes. A reverse-infection algorithm is proposed to find such an estimator in general graphs. We prove that for $g$-regular trees such that $gq>1,$ where $g$ is the node degree and $q$ is the infection probability, the estimator is within a constant distance from the actual source with a high probability, independent of the number of infected nodes and the time the snapshot is taken. Our simulation results show that for tree networks, the estimator produced by the reverse-infection algorithm is closer to the actual source than the one identified by the closeness centrality heuristic. We then further evaluate the performance of the reverse infection algorithm on several real world networks.

Connected through hyperedge HE-1 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media"):
  with members:
    - "an end to end framework to identify pathogenic social media accounts on twitter"
    - "detection of violent extremists in social media"
    - "detecting pathogenic social media accounts without content or network structure"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "deep neural networks for bot detection"
    - "early identification of pathogenic social media accounts"

Connected through hyperedge HE-2 (induced by source paper "gfcn a new graph convolutional network based on parallel flows"):
  with members:
    - "graph normalizing flows"
    - "adaptive graph convolutional neural networks"
    - "topology adaptive graph convolutional networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-3 (induced by source paper "hawkes process for understanding the influence of pathogenic social media accounts"):
  with members:
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "the spread of low credibility content by social bots"
    - "semi supervised learning for detecting human trafficking"
    - "the spread of fake news by social bots"

Connected through hyperedge HE-4 (induced by source paper "graphflow a new graph convolutional network based on parallel flows"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "topology adaptive graph convolutional networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "graph learning from data under laplacian and structural constraints"
    - "graph based convolutional neural network"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "approximate identification of the optimal epidemic source in complex networks"):
  with members:
    - "the spread of low credibility content by social bots"
    - "dandelion redesigning the bitcoin network for anonymity"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "the spread of fake news by social bots"
    - "multiple infection sources identification with provable guarantees"
    - "identification of patient zero in static and temporal networks robustness and limitations"
    - "how to identify an infection source with limited observations"
    - "social structure of facebook networks"

Connected through hyperedge HE-6 (induced by source paper "information source detection with limited time knowledge"):
  with members:
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "estimating infection sources in networks using partial timestamps"
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "multiple infection sources identification with provable guarantees"
    - "hiding the rumor source"
    - "how to identify an infection source with limited observations"
    - "a robust information source estimator with sparse observations"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-7 (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"
    - "rooting out the rumor culprit from suspects"
    - "finding an infection source under the sis model"

Connected through hyperedge HE-8 (induced by source paper "estimating infection sources in networks using partial timestamps"):
  with members:
    - "optimal localization of diffusion sources in complex networks"
    - "modeling the propagation of trojan malware in online social networks"
    - "infection spreading and source identification a hide and seek game"
    - "source localization in networks trees and beyond"
    - "how to identify an infection source with limited observations"
    - "spy vs spy rumor source obfuscation"
    - "identifying infection sources and regions in large networks"
    - "a robust information source estimator with sparse observations"

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)