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: "identifying infection sources and regions in large networks"
  Abstract: Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each source) in a network, based only on knowledge of which nodes are infected and their connections, and when the number of sources is unknown a priori. We derive estimators for the infection sources and their infection regions based on approximations of the infection sequences count. We prove that if there are at most two infection sources in a geometric tree, our estimator identifies the true source or sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, and when the maximum possible number of infection sources is known, we propose an algorithm with quadratic complexity to estimate the actual number and identities of the infection sources. Simulations on various kinds of networks, including tree networks, small-world networks and real world power grid networks, and tests on two real data sets are provided to verify the performance of our estimators.

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"
    - "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-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"
    - "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-3 (induced by source paper "on privacy of socially contagious attributes"):
  with members:
    - "ties that bind characterizing classes by attributes and social ties"
    - "pufferfish privacy mechanisms for correlated data"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "multiple infection sources identification with provable guarantees"
    - "maximizing social influence in nearly optimal time"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-4 (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-5 (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"
    - "a robust information source estimator with sparse observations"
    - "finding an infection source under the sis model"

Connected through hyperedge HE-6 (induced by source paper "csi a hybrid deep model for fake news detection"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "distributed representations of sentences and documents"
    - "tweetcred real time credibility assessment of content on twitter"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "sequence to sequence learning with neural networks"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-7 (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"
    - "a robust information source estimator with sparse observations"
    - "rooting out the rumor culprit from suspects"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-8 (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"
    - "rooting out the rumor culprit from suspects"

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.DM(Discrete Mathematics)