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: "look imagine and match improving textual visual cross modal retrieval with generative models"
  Abstract: Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.

Connected through hyperedge HE-1 (induced by source paper "latent normalizing flows for many to many cross domain mappings"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "preventing posterior collapse with delta vaes"
    - "mirrorgan learning text to image generation by redescription"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "m3d gan multi modal multi domain translation with universal attention"
    - "semantics disentangling for text to image generation"
    - "describing like humans on diversity in image captioning"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"

Connected through hyperedge HE-2 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"

Connected through hyperedge HE-3 (induced by source paper "mhsan multi head self attention network for visual semantic embedding"):
  with members:
    - "univse robust visual semantic embeddings via structured semantic representations"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bam bottleneck attention module"
    - "stacked cross attention for image text matching"
    - "cbam convolutional block attention module"
    - "simple recurrent units for highly parallelizable recurrence"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "expressing objects just like words recurrent visual embedding for image text matching"):
  with members:
    - "position focused attention network for image text matching"
    - "stacked cross attention for image text matching"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-5 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "use what you have video retrieval using representations from collaborative experts"
    - "language conditioned graph networks for relational reasoning"
    - "simple bert models for relation extraction and semantic role labeling"
    - "evaluating text to image matching using binary image selection bison"
    - "relation aware graph attention network for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-6 (induced by source paper "a convolutional baseline for person re identification using vision and language descriptions"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "attention aware compositional network for person re identification"
    - "stacked cross attention for image text matching"
    - "pose guided joint global and attentive local matching network for text based person search"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-7 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "visual semantic reasoning for image text matching"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "visual reasoning with multi hop feature modulation"
    - "identity aware textual visual matching with latent co attention"

Connected through hyperedge HE-8 (induced by source paper "deep multimodal image text embeddings for automatic cross media retrieval"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "stacked cross attention for image text matching"
    - "twitter100k a real world dataset for weakly supervised cross media retrieval"
    - "dual path convolutional image text embedding with instance loss"
    - "leveraging visual question answering for image caption ranking"
    - "aggregated residual transformations for deep neural networks"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "linking image and text with 2 way nets"

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)