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 "cooperative image captioning"):
  Abstract: When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a "speaker network" that generates sentences given an image, and a "listener network" that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate to achieve a joint task, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient. Second, joint training usually causes the vocabulary used during communication to drift and diverge from natural language. #R##N#We describe an approach that addresses both challenges. We first develop a new effective optimization based on partial-sampling from a multinomial distribution combined with straight-through gradient updates, which we name PSST for Partial-Sampling Straight-Through. Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions. Together, this approach creates descriptions that are both more discriminative and more natural than previous approaches. Evaluations on the standard COCO benchmark show that PSST Multinomial dramatically improve the recall@10 from 60% to 86% maintaining comparable language naturalness, and human evaluations show that it also increases naturalness while keeping the discriminative power of generated captions.

Includes members:
  Member "binary image selection bison interpretable evaluation of visual grounding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "evaluating text to image matching using binary image selection bison":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning")
  Member "a neural compositional paradigm for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning to describe differences between pairs of similar images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adjusting image attributes of localized regions with low level dialogue")
  Member "how agents see things on visual representations in an emergent language game":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards graph representation learning in emergent communication")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "rebar low variance unbiased gradient estimates for discrete latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "natural language does not emerge naturally in multi agent dialog":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the interaction between supervision and self play in emergent communication")

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)