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 "active classification with comparison queries"):
  Abstract: We study an extension of active learning in which the learning algorithm may ask the annotator to compare the distances of two examples from the boundary of their label-class. For example, in a recommendation system application (say for restaurants), the annotator may be asked whether she liked or disliked a specific restaurant (a label query); or which one of two restaurants did she like more (a comparison query). #R##N#We focus on the class of half spaces, and show that under natural assumptions, such as large margin or bounded bit-description of the input examples, it is possible to reveal all the labels of a sample of size $n$ using approximately $O(\log n)$ queries. This implies an exponential improvement over classical active learning, where only label queries are allowed. We complement these results by showing that if any of these assumptions is removed then, in the worst case, $\Omega(n)$ queries are required. #R##N#Our results follow from a new general framework of active learning with additional queries. We identify a combinatorial dimension, called the \emph{inference dimension}, that captures the query complexity when each additional query is determined by $O(1)$ examples (such as comparison queries, each of which is determined by the two compared examples). Our results for half spaces follow by bounding the inference dimension in the cases discussed above.

Includes members:
  Member "interactive bayesian hierarchical clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery")
  Member "dimension coupling optimal active learning of halfspaces via query synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active decision boundary annotation with deep generative models")
  Member "clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainable k means and k medians clustering")
  Member "the optimal sample complexity of pac learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond dropout feature map distortion to regularize deep neural networks")
  Member "beyond disagreement based agnostic active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive region based active learning")
  Member "a compression technique for analyzing disagreement based active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep active learning with a neural architecture search")
  Member "minimax analysis of active learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive region based active learning")
  Member "learning using local membership queries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the hardness of robust classification")

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)