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: "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
  Abstract: Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies and benchmarks are still not clear in the literatures. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges and open issues, as well as build up the benchmarks including datasets and experimental results. Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods. This will help them to focus on algorithm design, rather than the time-consuming compared methods and results. It is noted that we have constructed a new dataset XMedia, which is the first publicly available dataset with up to five media types (text, image, video, audio and 3D model). We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.

Connected through hyperedge HE-1 (induced by source paper "learning cross modal embeddings with adversarial networks for cooking recipes and food images"):
  with members:
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "can a chatbot determine my diet addressing challenges of chatbot application for meal recommendation"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "is saki delicious the food perception gap on instagram and its relation to health"

Connected through hyperedge HE-2 (induced by source paper "huse hierarchical universal semantic embeddings"):
  with members:
    - "adversarial representation learning for text to image matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning structured semantic embeddings for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "triplet based deep hashing network for cross modal retrieval"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "a new benchmark and approach for fine grained cross media retrieval"):
  with members:
    - "fine grained video categorization with redundancy reduction attention"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "fine grained image classification via combining vision and language"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "performance effectiveness of multimedia information search using the epsilon greedy algorithm"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "term relevance feedback for contextual named entity retrieval"
    - "darts differentiable architecture search"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "hierarchical representations for efficient architecture search"
    - "relevance based word embedding"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-6 (induced by source paper "effective and efficient indexing in cross modal hashing based datasets"):
  with members:
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "discrete latent factor model for cross modal hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-7 (induced by source paper "canonical correlation analysis cca based multi view learning an overview"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "deep variational canonical correlation analysis"
    - "correlational neural networks"
    - "a survey on multi view learning"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-8 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "image pivoting for learning multilingual multimodal representations"
    - "stair captions constructing a large scale japanese image caption dataset"

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.MM(Multimedia)