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 "correlation clustering with adaptive similarity queries"):
  Abstract: In correlation clustering, we are given $n$ objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity score can be learned by making a query, and the goal is to minimise both the disagreements and the total number of queries. On the one hand, we describe simple active learning algorithms, which provably achieve an almost optimal trade-off while giving cluster recovery guarantees, and we test them on different datasets. On the other hand, we prove information-theoretical bounds on the number of queries necessary to guarantee a prescribed disagreement bound. These results give a rich characterization of the trade-off between queries and clustering error.

Includes members:
  Member "correlation clustering with same cluster queries bounded by optimal cost":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "query efficient correlation clustering")
  Member "predicting positive and negative links with noisy queries theory practice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "same cluster querying for overlapping clusters")
  Member "approximate correlation clustering using same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "community recovery in graphs with locality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "community recovery in hypergraphs")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "on the complexity of learning with kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tight kernel query complexity of kernel ridge regression and kernel k means clustering")
  Member "local correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "query efficient correlation clustering")
  Member "community detection thresholds and the weak ramanujan property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "from tree matching to sparse graph alignment")

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)