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: "multiple infection sources identification with provable guarantees"
  Abstract: Given an aftermath of a cascade in the network, i.e. a set $V_I$ of "infected" nodes after an epidemic outbreak or a propagation of rumors/worms/viruses, how can we infer the sources of the cascade? Answering this challenging question is critical for computer forensic, vulnerability analysis, and risk management. Despite recent interest towards this problem, most of existing works focus only on single source detection or simple network topologies, e.g. trees or grids. #R##N#In this paper, we propose a new approach to identify infection sources by searching for a seed set $S$ that minimizes the \emph{symmetric difference} between the cascade from $S$ and $V_I$, the given set of infected nodes. Our major result is an approximation algorithm, called SISI, to identify infection sources \emph{without the prior knowledge on the number of source nodes}. SISI, to our best knowledge, is the first algorithm with \emph{provable guarantee} for the problem in general graphs. It returns a $\frac{2}{(1-\epsilon)^2}\Delta$-approximate solution with high probability, where $\Delta$ denotes the maximum number of nodes in $V_I$ that may infect a single node in the network. Our experiments on real-world networks show the superiority of our approach and SISI in detecting true source(s), boosting the F1-measure from few percents, for the state-of-the-art NETSLEUTH, to approximately 50\%.

Connected through hyperedge HE-1 (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"
    - "identification of patient zero in static and temporal networks robustness and limitations"
    - "how to identify an infection source with limited observations"
    - "information source detection in the sir model a sample path based approach"
    - "social structure of facebook networks"

Connected through hyperedge HE-2 (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"
    - "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-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"
    - "identifying infection sources and regions in large networks"
    - "maximizing social influence in nearly optimal time"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-4 (induced by source paper "importance sketching of influence dynamics in billion scale networks"):
  with members:
    - "outward influence and cascade size estimation in billion scale networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "outward influence and cascade size estimation in billion scale networks"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"

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)