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: "random spanning trees and the prediction of weighted graphs"
  Abstract: We investigate the problem of sequentially predicting the binary labels on the nodes of an arbitrary weighted graph. We show that, under a suitable parametrization of the problem, the optimal number of prediction mistakes can be characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the graph. The cutsize is induced by the unknown adversarial labeling of the graph nodes. In deriving our characterization, we obtain a simple randomized algorithm achieving in expectation the optimal mistake bound on any polynomially connected weighted graph. Our algorithm draws a random spanning tree of the original graph and then predicts the nodes of this tree in constant expected amortized time and linear space. Experiments on real-world datasets show that our method compares well to both global (Perceptron) and local (label propagation) methods, while being generally faster in practice.

Connected through hyperedge HE-1 (induced by source paper "online similarity prediction of networked data from known and unknown graphs"):
  with members:
    - "active learning on trees and graphs"
    - "see the tree through the lines the shazoo algorithm"
    - "near optimal algorithms for online matrix prediction"
    - "generalization bounds for metric and similarity learning"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "regularization techniques for learning with matrices"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "a correlation clustering approach to link classification in signed networks full version"):
  with members:
    - "active learning on trees and graphs"
    - "see the tree through the lines the shazoo algorithm"
    - "the slashdot zoo mining a social network with negative edges"
    - "transductive rademacher complexity and its applications"
    - "a tutorial on spectral clustering"
    - "correlation clustering with a fixed number of clusters"
    - "lower stretch spanning trees"

Connected through hyperedge HE-3 (induced by source paper "a linear time active learning algorithm for link classification full version"):
  with members:
    - "active learning on trees and graphs"
    - "see the tree through the lines the shazoo algorithm"
    - "the slashdot zoo mining a social network with negative edges"
    - "a tutorial on spectral clustering"
    - "lower stretch spanning trees"

Connected through hyperedge HE-4 (induced by source paper "see the tree through the lines the shazoo algorithm full version"):
  with members:
    - "active learning on trees and graphs"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-5 (induced by source paper "active learning on trees and graphs"):
  with members:
    - "see the tree through the lines the shazoo algorithm"

Connected through hyperedge HE-6 (induced by source paper "see the tree through the lines the shazoo algorithm"):
  with members:
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-7 (induced by source paper "a scalable multiclass algorithm for node classification"):
  with members:
    - "see the tree through the lines the shazoo algorithm"

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)