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: "analysis of the impact of negative sampling on link prediction in knowledge graphs"
  Abstract: Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure of the graph. This has inspired methods for the joint embedding of entities and relations in continuous low-dimensional vector spaces, that can be used to induce new edges in the graph, i.e., link prediction in knowledge graphs. Learning these representations relies on contrasting positive instances with negative ones. Knowledge graphs include only positive relation instances, leaving the door open for a variety of methods for selecting negative examples. In this paper we present an empirical study on the impact of negative sampling on the learned embeddings, assessed through the task of link prediction. We use state-of-the-art knowledge graph embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on benchmark datasets -- FB15k and WN18. We compare well known methods for negative sampling and additionally propose embedding based sampling methods. We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.

Connected through hyperedge HE-1 (induced by source paper "network representation learning for link prediction are we improving upon simple heuristics"):
  with members:
    - "network representation learning consolidation and renewed bearing"
    - "evalne a framework for evaluating network embeddings on link prediction"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "watch your step learning node embeddings via graph attention"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "probability calibration for knowledge graph embedding models"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "quaternion knowledge graph embedding"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network"):
  with members:
    - "detecting drug drug interactions using artificial neural networks and classic graph similarity measures"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "pytorch biggraph a large scale graph embedding system"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "predicting rich drug drug interactions via biomedical knowledge graphs and text jointly embedding"
    - "complex embeddings for simple link prediction"
    - "knowlege graph embedding by flexible translation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "improving skip gram based graph embeddings via centrality weighted sampling"):
  with members:
    - "network representation learning a survey"
    - "node2vec scalable feature learning for networks"
    - "tensorflow a system for large scale machine learning"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "distributional negative sampling for knowledge base completion"):
  with members:
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "nseen neural semantic embedding for entity normalization"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "biomedical named entity recognition via reference set augmented bootstrapping"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "harp hierarchical representation learning for networks"
    - "training region based object detectors with online hard example mining"
    - "non metric space library manual"

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)