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: "embedding multimodal relational data for knowledge base completion"
  Abstract: Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base. We enrich existing relational datasets to create two novel benchmarks that contain additional information such as textual descriptions and images of the original entities. We demonstrate that our models utilize this additional information effectively to provide more accurate link prediction, achieving state-of-the-art results with a considerable gap of 5-7% over existing methods. Further, we evaluate the quality of our generated multimodal values via a user study. We have release the datasets and the open-source implementation of our models at this https URL

Connected through hyperedge HE-1 (induced by source paper "a new codebook design for analog beamforming in millimeter wave communication"):
  with members:
    - "investigating robustness and interpretability of link prediction via adversarial modifications"
    - "partition pruning parallelization aware pruning for deep neural networks"
    - "age of information in multiple sensing"
    - "on the age of information in erasure channels with feedback"
    - "generating user friendly explanations for loan denials using gans"
    - "hybrid precoder and combiner design with low resolution phase shifters in mmwave mimo systems"
    - "on the performance of mrc receiver with unknown timing mismatch a large scale analysis"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "a first experiment on including text literals in kglove"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "multi task neural network for non discrete attribute prediction in knowledge graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "investigating robustness and interpretability of link prediction via adversarial modifications"):
  with members:
    - "understanding the origins of bias in word embeddings"
    - "adversarial attack on graph structured data"
    - "graph embedding techniques applications and performance a survey"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "adversarial sets for regularising neural link predictors"

Connected through hyperedge HE-4 (induced by source paper "mmkg multi modal knowledge graphs"):
  with members:
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "a neural knowledge language model"
    - "the more you know using knowledge graphs for image classification"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "answering visual relational queries in web extracted knowledge graphs"):
  with members:
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "learning to disambiguate by asking discriminative questions"
    - "deep metric learning with angular loss"
    - "a neural knowledge language model"
    - "knowledge base completion baselines strike back"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "deep metric learning via facility location"
    - "graph structured representations for 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.AI(Artificial Intelligence)