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: "back to the past source identification in diffusion networks from partially observed cascades"
  Abstract: When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a two-stage framework, which first learns a continuous-time diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and real-world data show that our framework can effectively go back to the past, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "cascade size distributions and why they matter"):
  with members:
    - "fake news detection in social networks via crowd signals"
    - "balancing information exposure in social networks"
    - "mitigating overexposure in viral marketing"
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "online influence maximization under independent cascade model with semi bandit feedback"

Connected through hyperedge HE-2 (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"
    - "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"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-3 (induced by source paper "robust cascade reconstruction by steiner tree sampling"):
  with members:
    - "reconstructing a cascade from temporal observations"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-4 (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"
    - "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"
    - "rooting out the rumor culprit from suspects"
    - "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 "rumor source detection under querying with untruthful answers"):
  with members:
    - "a robust information source estimator with sparse observations"
    - "rooting out the rumor culprit from suspects"
    - "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-6 (induced by source paper "online bayesian inference of diffusion networks"):
  with members:
    - "bayesian inference of diffusion networks with unknown infection times"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-7 (induced by source paper "bayesian inference of diffusion networks with unknown infection times"):
  with members:
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-8 (induced by source paper "spatio temporal modeling of users check ins in location based social networks"):
  with members:
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "latent self exciting point process model for spatial temporal networks"
    - "modeling information propagation with survival theory"

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)