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 "crowdsourced correlation clustering with relative distance comparisons"):
  Abstract: Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information. A relative distance comparison is a statement of the form "item A is closer to item B than to item C". However, many existing clustering algorithms that use relative distances are rather complex. They are often based on a two-step approach, where the relative distances are first used to learn either a distance matrix, or an embedding of the items, and then some standard clustering method is applied in a second step. In this paper we argue that it should be possible to compute a clustering directly from relative distance comparisons. Our ideas are built upon existing work on correlation clustering, a well-known non-parametric approach to clustering. The technical contribution of this work is twofold. We first define a novel variant of correlation clustering that is based on relative distance comparisons, and hence suitable for human computation. We go on to show that our new problem is closely related to basic correlation clustering, and use this property to design an approximation algorithm for our problem. As a second contribution, we propose a more practical algorithm, which we empirically compare against existing methods from literature. Experiments with synthetic data suggest that our approach can outperform more complex methods. Also, our method efficiently finds good and intuitive clusterings from real relative distance comparison data.

Includes members:
  Member "t exponential triplet embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "cost effective hits for relative similarity comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "adaptively learning the crowd kernel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretable off policy evaluation in reinforcement learning by highlighting influential transitions")

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.DS(Data Structures and Algorithms)