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: "on the universality of jordan centers for estimating infection sources in tree networks"
  Abstract: Finding the infection sources in a network when we only know the network topology and infected nodes, but not the rates of infection, is a challenging combinatorial problem, and it is even more difficult in practice where the underlying infection spreading model is usually unknown  a priori . In this paper, we are interested in finding a source estimator that is applicable to various spreading models, including the susceptible–infected (SI), susceptible–infected–recovered (SIR), susceptible–infected–recovered–infected (SIRI), and susceptible–infected–susceptible (SIS) models. We show that under the SI, SIR, and SIRI spreading models and with mild technical assumptions, the Jordan center is the infection source associated with the most likely infection path in a tree network with a single infection source. This conclusion applies for a wide range of spreading parameters, while it holds for regular trees under the SIS model with homogeneous infection and recovery rates. Since the Jordan center does not depend on the infection, recovery, and reinfection rates, it can be regarded as a  universal  source estimator. We also consider the case where there are    $k>1$    infection sources, generalize the Jordan center definition to a    $k$   -Jordan center set, and show that this is an optimal infection source set estimator in a tree network for the SI model. Simulation results on various general synthetic networks and real-world networks suggest that Jordan center-based estimators consistently outperform the betweenness, closeness, distance, degree, eigenvector, and pagerank centrality-based heuristics, even if the network is not a tree.

Connected through hyperedge HE-1 (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"
    - "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"
    - "graph based convolutional neural network"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-3 (induced by source paper "information source detection with limited time knowledge"):
  with members:
    - "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"
    - "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 "infection spreading and source identification a hide and seek game"):
  with members:
    - "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"
    - "rooting out the rumor culprit from suspects"

Connected through hyperedge HE-5 (induced by source paper "catch em all locating multiple diffusion sources in networks with partial observations"):
  with members:
    - "source localization in networks trees and beyond"
    - "identifying infection sources and regions in large networks"
    - "a robust information source estimator with sparse observations"
    - "locating the source of diffusion in large scale 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)