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: "evaluating link prediction methods"
  Abstract: Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed for link prediction, we argue that the practical performance potential of these methods is often unknown because of challenges in the evaluation of link prediction, which impact the reliability and reproducibility of results. We describe these challenges, provide theoretical proofs and empirical examples demonstrating how current methods lead to questionable conclusions, show how the fallacy of these conclusions is illuminated by methods we propose, and develop recommendations for consistent, standard, and applicable evaluation metrics. We also recommend the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to complications that arise from extreme imbalance in the link prediction classification problem.

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"
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "e lstm d a deep learning framework for dynamic network link prediction"):
  with members:
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deepwalk online learning of social representations"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "link prediction via higher order motif features"):
  with members:
    - "link prediction based on graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "link prediction in social networks the state of the art"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-4 (induced by source paper "personalized degrees effects on link formation in dynamic networks from an egocentric perspective"):
  with members:
    - "link prediction with node clustering coefficient"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "social dynamics of digg"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "applications of multi view learning approaches for software comprehension"):
  with members:
    - "codesum translate program language to natural language"
    - "a survey of machine learning for big code and naturalness"
    - "on the effect of semantically enriched context models on software modularization"
    - "a survey on multi view learning"

Connected through hyperedge HE-6 (induced by source paper "automatic discovery of families of network generative processes"):
  with members:
    - "symbolic regression of generative network models"
    - "generating constrained random graphs using multiple edge switches"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-7 (induced by source paper "detecting drug drug interactions using artificial neural networks and classic graph similarity measures"):
  with members:
    - "neural collaborative filtering"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "mining temporal evolution of knowledge graph and genealogical features for literature based discovery prediction"):
  with members:
    - "tracing community genealogy how new communities emerge from the old"
    - "entitymetrics measuring the impact of entities"

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.IR(Information Retrieval)