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: "type constrained representation learning in knowledge graphs"
  Abstract: Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.

Connected through hyperedge HE-1 (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"
    - "embedding multimodal relational data for knowledge base completion"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "pytorch biggraph a large scale graph embedding system"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mile a multi level framework for scalable graph embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"
    - "swivel improving embeddings by noticing what s missing"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "knowledge graph fact prediction via knowledge enriched tensor factorization"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "adversarial sets for regularising neural link predictors"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"
    - "complex embeddings for simple link prediction"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "rule guided compositional representation learning on knowledge graphs"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "convolutional 2d knowledge graph embeddings"
    - "harnessing deep neural networks with logic rules"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-5 (induced by source paper "interaction embeddings for prediction and explanation in knowledge graphs"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "analogical inference for multi relational embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "neural generative question answering"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "canonicalizing knowledge base literals"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "colnet embedding the semantics of web tables for column type prediction"
    - "mapping text to knowledge graph entities using multi sense lstms"
    - "quality assessment of linked datasets using probabilistic approximation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (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-8 (induced by source paper "encoding implicit relation requirements for relation extraction a joint inference approach"):
  with members:
    - "jointly extracting relations with class ties via effective deep ranking"

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)