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: "see the tree through the lines the shazoo algorithm"
  Abstract: Predicting the nodes of a given graph is a fascinating theoretical problem with applications in several domains. Since graph sparsification via spanning trees retains enough information while making the task much easier, trees are an important special case of this problem. Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet. We fill this hole and introduce an efficient node predictor, Shazoo, which is nearly optimal on any weighted tree. Moreover, we show that Shazoo can be viewed as a common nontrivial generalization of both previous approaches for unweighted trees and weighted lines. Experiments on real-world datasets confirm that Shazoo performs well in that it fully exploits the structure of the input tree, and gets very close to (and sometimes better than) less scalable energy minimization methods.

Connected through hyperedge HE-1 (induced by source paper "online prediction of switching graph labelings with cluster specialists"):
  with members:
    - "strongly adaptive online learning"
    - "mirror descent meets fixed share and feels no regret"

Connected through hyperedge HE-2 (induced by source paper "predicting switching graph labelings with cluster specialists"):
  with members:
    - "strongly adaptive online learning"

Connected through hyperedge HE-3 (induced by source paper "on pairwise clustering with side information"):
  with members:
    - "active learning on trees and graphs"
    - "online similarity prediction of networked data from known and unknown graphs"
    - "generalization bounds for metric and similarity learning"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "online similarity prediction of networked data from known and unknown graphs"):
  with members:
    - "active learning on trees and graphs"
    - "near optimal algorithms for online matrix prediction"
    - "random spanning trees and the prediction of weighted graphs"
    - "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-5 (induced by source paper "a correlation clustering approach to link classification in signed networks full version"):
  with members:
    - "active learning on trees and graphs"
    - "random spanning trees and the prediction of weighted graphs"
    - "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-6 (induced by source paper "a linear time active learning algorithm for link classification full version"):
  with members:
    - "active learning on trees and graphs"
    - "random spanning trees and the prediction of weighted graphs"
    - "the slashdot zoo mining a social network with negative edges"
    - "a tutorial on spectral clustering"
    - "lower stretch spanning trees"

Connected through hyperedge HE-7 (induced by source paper "active learning on trees and graphs"):
  with members:
    - "random spanning trees and the prediction of weighted graphs"

Connected through hyperedge HE-8 (induced by source paper "random spanning trees and the prediction of weighted graphs"):
  with members:
    - "active learning on trees and graphs"
    - "low congestion online routing and an improved mistake bound for online prediction of graph labeling"
    - "graph sparsification by effective resistances"

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)