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 "informative object annotations tell me something i don t know"):
  Abstract: Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and utilize this to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves ~65% agreement with human raters, largely outperforming other unsupervised baseline approaches.

Includes members:
  Member "answerer in questioner s mind information theoretic approach to goal oriented visual dialog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guessing state tracking for visual dialogue")
  Member "discriminability objective for training descriptive captions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning visual dialogs with structural and partial observations")
  Member "the open images dataset v4 unified image classification object detection and visual relationship detection at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting the sibling head in object detector")
  Member "efficient human like semantic representations via the information bottleneck principle":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "specializing word embeddings for parsing by information bottleneck")
  Member "context aware captions from context agnostic supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarially guided self play for adopting social conventions")
  Member "reasoning about pragmatics with neural listeners and speakers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "emergence of pragmatics from referential game between theory of mind agents")
  Member "salient object detection a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")

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.CV(Computer Vision and Pattern Recognition)