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 "tribeflow mining predicting user trajectories"):
  Abstract: Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.

Includes members:
  Member "a scalable asynchronous distributed algorithm for topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking")
  Member "revisit behavior in social media the phoenix r model and discoveries":
    also connected through other hyperedges:
      (none)
  Member "dj mc a reinforcement learning agent for music playlist recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model based reinforcement learning with adversarial training for online recommendation")
  Member "scalable recommendation with poisson factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint neural collaborative filtering for recommender systems")
  Member "la lda a limited attention topic model for social recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "la ctr a limited attention collaborative topic regression for social media")
  Member "la ctr a limited attention collaborative topic regression for social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vip incorporating human cognitive biases in a probabilistic model of retweeting")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "supervised random walks predicting and recommending links in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")

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)