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: "answering visual relational queries in web extracted knowledge graphs"
  Abstract: A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs. To this end, we have created ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images crawled from the web. With visual-relational KGs such as ImageGraph one can introduce novel probabilistic query types in which images are treated as first-class citizens. Both the prediction of relations between unseen images as well as multi-relational image retrieval can be expressed with specific families of visual-relational queries. We introduce novel combinations of convolutional networks and knowledge graph embedding methods to answer such queries. We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation. We conduct experiments to demonstrate that the proposed methods can answer these visual-relational queries efficiently and accurately.

Connected through hyperedge HE-1 (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"
    - "embedding multimodal relational data for knowledge base completion"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "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-2 (induced by source paper "mmkg multi modal knowledge graphs"):
  with members:
    - "embedding multimodal relational data for knowledge base completion"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "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-3 (induced by source paper "embedding multimodal relational data for knowledge base completion"):
  with members:
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "self normalizing neural networks"
    - "adversarially regularized autoencoders"
    - "long text generation via adversarial training with leaked information"
    - "convolutional 2d knowledge graph embeddings"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "knowledge based distant regularization in learning probabilistic models"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "learning the structure of generative models without labeled data"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "fast robust pca on graphs"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

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.LG(Machine Learning)