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 "retrospective higher order markov processes for user trails"):
  Abstract: Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such sequences of data. First-order Markov chains are easy to estimate, but lack accuracy when history matters. Higher-order Markov chains, in contrast, have too many parameters and suffer from overfitting the training data. Fitting these parameters with regularization and smoothing only offers mild improvements. In this paper we propose the retrospective higher-order Markov process (RHOMP) as a low-parameter model for such sequences. This model is a special case of a higher-order Markov chain where the transitions depend retrospectively on a single history state instead of an arbitrary combination of history states. There are two immediate computational advantages: the number of parameters is linear in the order of the Markov chain and the model can be fit to large state spaces. Furthermore, by providing a specific structure to the higher-order chain, RHOMPs improve the model accuracy by efficiently utilizing history states without risks of overfitting the data. We demonstrate how to estimate a RHOMP from data and we demonstrate the effectiveness of our method on various real application datasets spanning geolocation data, review sequences, and business locations. The RHOMP model uniformly outperforms higher-order Markov chains, Kneser-Ney regularization, and tensor factorizations in terms of prediction accuracy.

Includes members:
  Member "the spacey random walk a stochastic process for higher order data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlocal pagerank")
  Member "linear additive markov processes":
    also connected through other hyperedges:
      (none)
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "general tensor spectral co clustering for higher order data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs")
  Member "the new data and new challenges in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale weakly supervised pre training for video action recognition")
  Member "tensor spectral clustering for partitioning higher order network structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "tribeflow mining predicting user trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data")
  Member "hyptrails a bayesian approach for comparing hypotheses about human trails on the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting path anomalies in time series data on networks")

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)