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: "image embodied knowledge representation learning"
  Abstract: Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

Connected through hyperedge HE-1 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-3 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-4 (induced by source paper "multimodal grounding for language processing"):
  with members:
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "multimodal named entity recognition for short social media posts"
    - "modulating early visual processing by language"
    - "multimodal machine learning a survey and taxonomy"
    - "gated attention architectures for task oriented language grounding"
    - "emergence of grounded compositional language in multi agent populations"
    - "teaching machines to describe images via natural language feedback"

Connected through hyperedge HE-5 (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"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "logic attention based neighborhood aggregation for inductive knowledge graph embedding"):
  with members:
    - "one shot relational learning for knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "open world knowledge graph completion"
    - "learning graph representations with embedding propagation"
    - "analogical inference for multi relational embeddings"
    - "revisiting semi supervised learning with graph embeddings"
    - "complex embeddings for simple link prediction"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-7 (induced by source paper "incorporating literals into knowledge graph embeddings"):
  with members:
    - "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"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "convolutional 2d knowledge graph embeddings"
    - "knowledge graph representation with jointly structural and textual encoding"
    - "complex embeddings for simple link prediction"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "incorporating gan for negative sampling in knowledge representation learning"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "knowledge graph representation with jointly structural and textual encoding"
    - "facenet a unified embedding for face recognition and clustering"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "adam a method for stochastic optimization"
    - "a semantic matching energy function for learning with multi relational data"

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)