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 user specific latent influence and susceptibility from information cascades"
  Abstract: Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".

Connected through hyperedge HE-1 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "topological recurrent neural network for diffusion prediction"):
  with members:
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "recurrent neural network grammars"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "the simple rules of social contagion"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "activating the breakfast club modeling influence spread in natural world social networks"):
  with members:
    - "greed is good near optimal submodular maximization via greedy optimization"

Connected through hyperedge HE-4 (induced by source paper "learning sentimental influences from users behaviors"):
  with members:
    - "modeling information propagation with survival theory"
    - "opinion maximization in social networks"
    - "distributed representations of words and phrases and their compositionality"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "adadelta an adaptive learning rate method"
    - "structure and dynamics of information pathways in online media"
    - "how bad is forming your own opinion"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-5 (induced by source paper "a comparison of methods for cascade prediction"):
  with members:
    - "toward order of magnitude cascade prediction"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "predicting successful memes using network and community structure"
    - "a data based approach to social influence maximization"

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)