human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dialog based interactive image retrieval"
  Abstract: Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results. However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact. In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effective interaction. We formulate the task of dialog-based interactive image retrieval as a reinforcement learning problem, and reward the dialog system for improving the rank of the target image during each dialog turn. To mitigate the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train our system with a user simulator, which is itself trained to describe the differences between target and candidate images. The efficacy of our approach is demonstrated in a footwear retrieval application. Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.

Connected through hyperedge HE-1 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-3 (induced by source paper "fashion iq a new dataset towards retrieving images by natural language feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "robust change captioning"
    - "drill down interactive retrieval of complex scenes using natural language queries"
    - "expressing visual relationships via language"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "learning to describe differences between pairs of similar images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "person search with natural language description"

Connected through hyperedge HE-4 (induced by source paper "the fashion iq dataset retrieving images by combining side information and relative natural language feedback"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "composing text and image for image retrieval an empirical odyssey"
    - "person search with natural language description"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creating capsule wardrobes from fashion images"
    - "towards building large scale multimodal domain aware conversation systems"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"

Connected through hyperedge HE-5 (induced by source paper "drill down interactive retrieval of complex scenes using natural language queries"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "stacked cross attention for image text matching"
    - "chat crowd a dialog based platform for visual layout composition"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning fashion compatibility with bidirectional lstms"
    - "aligned image word representations improve inductive transfer across vision language tasks"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-6 (induced by source paper "let s transfer transformations of shared semantic representations"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "composing text and image for image retrieval an empirical odyssey"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "image generation from scene graphs"
    - "texturegan controlling deep image synthesis with texture patches"
    - "automatic spatially aware fashion concept discovery"
    - "adversarial discriminative domain adaptation"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-7 (induced by source paper "building task oriented visual dialog systems through alternative optimization between dialog policy and language generation"):
  with members:
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "sentiment adaptive end to end dialog systems"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "composing text and image for image retrieval an empirical odyssey"):
  with members:
    - "attributes as operators"
    - "mattnet modular attention network for referring expression comprehension"
    - "dynamic few shot visual learning without forgetting"
    - "in defense of the triplet loss for person re identification"
    - "language based image editing with recurrent attentive models"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "ivqa inverse visual question answering"

Question: Please tell me which class the center node 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)