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: "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
  Abstract: We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "differentiable reasoning on large knowledge bases and natural language"):
  with members:
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "towards neural theorem proving at scale"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "billion scale similarity search with gpus"
    - "end to end differentiable proving"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "coke contextualized knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "probabilistic logic neural networks for reasoning"
    - "improving knowledge graph embedding using simple constraints"
    - "deep contextualized word representations"
    - "recurrent one hop predictions for reasoning over knowledge graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "a re evaluation of knowledge graph completion methods"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kbgan adversarial learning for knowledge graph embeddings"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "embedding cardinality constraints in neural link predictors"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "cardinal virtues extracting relation cardinalities from text"
    - "predicting completeness in knowledge bases"
    - "adversarial sets for regularising neural link predictors"
    - "poincar e embeddings for learning hierarchical representations"
    - "complex embeddings for simple link prediction"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "mmkg multi modal knowledge graphs"):
  with members:
    - "embedding multimodal relational data for knowledge base completion"
    - "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"

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)