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: "learning influence functions from incomplete observations"
  Abstract: We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missing observations. Proper PAC learnability under the Discrete-Time Linear Threshold (DLT) and Discrete-Time Independent Cascade (DIC) models is established by reducing incomplete observations to complete observations in a modified graph. Our improper PAC learnability result applies for the DLT and DIC models as well as the Continuous-Time Independent Cascade (CIC) model. It is based on a parametrization in terms of reachability features, and also gives rise to an efficient and practical heuristic. Experiments on synthetic and real-world datasets demonstrate the ability of our method to compensate even for a fairly large fraction of missing observations.

Connected through hyperedge HE-1 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers"):
  with members:
    - "deepinf social influence prediction with deep learning"
    - "learning hawkes processes from short doubly censored event sequences"
    - "the social dynamics of language change in online networks"
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-2 (induced by source paper "discriminative modeling of social influence for prediction and explanation in event cascades"):
  with members:
    - "learning hawkes processes from short doubly censored event sequences"
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-3 (induced by source paper "adversarial influence maximization"):
  with members:
    - "online influence maximization with local observations"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "online influence maximization"
    - "influence maximization with bandits"

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)