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: "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.

Connected through hyperedge HE-1 (induced by source paper "retrospective higher order markov processes for user trails"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "linear additive markov processes"
    - "higher order organization of complex networks"
    - "general tensor spectral co clustering for higher order data"
    - "the new data and new challenges in multimedia research"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-2 (induced by source paper "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"):
  with members:
    - "what makes a link successful on wikipedia"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-3 (induced by source paper "neural network based next song recommendation"):
  with members:
    - "from word embeddings to item recommendation"
    - "classifying relations by ranking with convolutional neural networks"
    - "convolutional neural network architectures for matching natural language sentences"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

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)