human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a correlation clustering approach to link classification in signed networks full version"):
  Abstract: Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our main algorithmic contribution is in the active setting, where we introduce a new family of efficient link classifiers based on covering the input graph with small circuits. These are the first active algorithms for link classification with mistake bounds that hold for arbitrary signed networks.

Includes members:
  Member "active learning on trees and graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "see the tree through the lines the shazoo algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online prediction of switching graph labelings with cluster specialists")
  Member "random spanning trees and the prediction of weighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online similarity prediction of networked data from known and unknown graphs")
  Member "the slashdot zoo mining a social network with negative edges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep network embedding for graph representation learning in signed networks")
  Member "transductive rademacher complexity and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improvability through semi supervised learning a survey of theoretical results")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "correlation clustering with a fixed number of clusters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "lower stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "2 node connectivity network design")

Question: Please tell me which class the source paper 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)