human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "discriminative modeling of social influence for prediction and explanation in event cascades"):
  Abstract: The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging, due to confounds like homophily and practical issues like missing data. In this work, we propose a novel discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. Using synthetically generated data, we provide empirical evidence that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification --- unlike popular alternatives. We also apply the method to two real-world datasets: (1) cascades of co-sponsorship of legislation in the U.S. House of Representatives, on a social network of shared campaign donors; (2) rumors about the Higgs boson discovery, on a follower network of $10^5$ Twitter accounts. Our model identifies the role of peer influence in these scenarios, and uses it to make more accurate predictions about the future trajectory of cascades.

Includes members:
  Member "learning hawkes processes from short doubly censored event sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detection of outlier events in continuous time event sequences")
  Member "reconstructing parameters of spreading models from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep graph matching consensus")
  Member "learning granger causality for hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "learning influence functions from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers")
  Member "line large scale information network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction")
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep collaborative embedding for information cascade prediction")

Question: Please tell me which class the source paper 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)