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: "predicting adoption probabilities in social networks"
  Abstract: In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Na\"ive Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.

Connected through hyperedge HE-1 (induced by source paper "social influence deep learning for human behavior prediction"):
  with members:
    - "on the social influence in human behavior physical homophily and social communities"
    - "neural collaborative filtering"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "the call of the crowd event participation in location based social services"

Connected through hyperedge HE-2 (induced by source paper "tracking the diffusion of named entities"):
  with members:
    - "random voting effects in social digital spaces a case study of reddit post submissions"
    - "usfd twitter ner with drift compensation and linked data"
    - "temporal scaling in information propagation"
    - "evolution of reddit from the front page of the internet to a self referential community"
    - "navigating the massive world of reddit using backbone networks to map user interests in social media"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-3 (induced by source paper "temporal scaling in information propagation"):
  with members:
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "quantifying long term scientific impact"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "using a model of social dynamics to predict popularity of news"
    - "novelty and collective attention"

Connected through hyperedge HE-4 (induced by source paper "a survey of link recommendation for social networks methods theoretical foundations and future research directions"):
  with members:
    - "utility based link recommendation for online social networks"
    - "reciprocity in social networks with capacity constraints"
    - "user recommendations in reciprocal and bipartite social networks an online dating case study"
    - "negative link prediction in social media"
    - "multi scale link prediction"
    - "folks in folksonomies social link prediction from shared metadata"
    - "supervised random walks predicting and recommending links in social networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-5 (induced by source paper "utility based link recommendation for online social networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "the bayesian structural em algorithm"
    - "contrasting multiple social network autocorrelations for binary outcomes with applications to technology adoption"
    - "folks in folksonomies social link prediction from shared metadata"
    - "supervised random walks predicting and recommending links in social networks"
    - "the link prediction problem in bipartite networks"
    - "social networks that matter twitter under the microscope"

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)