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: "negative link prediction in social media"
  Abstract: Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "privacy in social media identification mitigation and applications"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "signed network modeling based on structural balance theory"
    - "semi supervised learning for detecting human trafficking"

Connected through hyperedge HE-2 (induced by source paper "deep network embedding for graph representation learning in signed networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "sitting closer to friends than enemies revisited"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-3 (induced by source paper "signed link prediction with sparse data the role of personality information"):
  with members:
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "semi supervised learning for detecting human trafficking"
    - "signed link analysis in social media networks"

Connected through hyperedge HE-4 (induced by source paper "centrality in dynamic competition networks"):
  with members:
    - "dynamic competition networks detecting alliances and leaders"
    - "the spatial ecology of war and peace"

Connected through hyperedge HE-5 (induced by source paper "dynamic competition networks detecting alliances and leaders"):
  with members:
    - "the spatial ecology of war and peace"
    - "dimensionality of social networks using motifs and eigenvalues"
    - "pagerank beyond the web"
    - "models for on line social networks"

Connected through hyperedge HE-6 (induced by source paper "signed node relevance measurements"):
  with members:
    - "understanding and predicting delay in reciprocal relations"
    - "a survey of signed network mining in social media"
    - "challenging the long tail recommendation"
    - "supervised random walks predicting and recommending links in social networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-7 (induced by source paper "collective vertex classification using recursive neural network"):
  with members:
    - "learning convolutional neural networks for graphs"
    - "deep learning in neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "a survey of signed network mining in social media"):
  with members:
    - "signed link analysis in social media networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "signed graph embedding when everybody can sit closer to friends than enemies"
    - "overlapping community detection in networks with positive and negative links"
    - "applications of structural balance in signed social networks"
    - "breaking the small cluster barrier of graph clustering"
    - "prediction and clustering in signed networks a local to global perspective"
    - "sitting closer to friends than enemies revisited"

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)