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: "cross modal retrieval in the cooking context learning semantic text image embeddings"
  Abstract: Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them. In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset containing nearly 1 million picture-recipe pairs. We show the effectiveness of our approach regarding previous state-of-the-art models and present qualitative results over computational cooking use cases.

Connected through hyperedge HE-1 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "unsupervised sentence representations as word information series revisiting tf idf"

Connected through hyperedge HE-2 (induced by source paper "cookgan meal image synthesis from ingredients"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "gilt generating images from long text"
    - "how to make a pizza learning a compositional layer based gan model"
    - "cgans with projection discriminator"
    - "a survey on food computing"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "cross modal food retrieval learning a joint embedding of food images and recipes with semantic consistency and attention mechanism"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "in defense of the triplet loss for person re identification"
    - "attention is all you need"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"
    - "image captioning with semantic attention"

Connected through hyperedge HE-5 (induced by source paper "how to make a pizza learning a compositional layer based gan model"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "composable unpaired image to image translation"
    - "multimodal unsupervised image to image translation"
    - "modular generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "ganimation anatomically aware facial animation from a single image"
    - "learning to see the invisible end to end trainable amodal instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "murel multimodal relational reasoning for visual question answering"):
  with members:
    - "question type guided attention in visual question answering"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-7 (induced by source paper "cooperative cross stream network for discriminative action representation"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "on the effectiveness of task granularity for transfer learning"
    - "video representation learning using discriminative pooling"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"

Connected through hyperedge HE-8 (induced by source paper "multidec multi modal clustering of image caption pairs"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "improving image clustering with multiple pretrained cnn feature extractors"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "learning robust visual semantic embeddings"
    - "cnn features are also great at unsupervised classification"
    - "ezlearn exploiting organic supervision in large scale data annotation"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "enriching word vectors with subword information"

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.CL(Computation and Language)