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: "on the troll trust model for edge sign prediction in social networks"
  Abstract: In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the troll-trust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple probabilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.

Connected through hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"

Connected through hyperedge HE-2 (induced by source paper "node classification for signed networks using diffuse interface methods"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "on spectral partitioning of signed graphs"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a survey of signed network mining in social media"
    - "vews a wikipedia vandal early warning system"

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.LG(Machine Learning)