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: "discriminative gaifman models"
  Abstract: We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally connected and bounded-size regions of knowledge bases (KBs). Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing. Gaifman models sample neighborhoods of knowledge bases so as to make the learned relational models more robust to missing objects and relations which is a common situation in open-world KBs. We present the core ideas of Gaifman models and apply them to large-scale relational learning problems. We also discuss the ways in which Gaifman models relate to some existing relational machine learning approaches.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-2 (induced by source paper "non parametric learning of gaifman models"):
  with members:
    - "dual convolutional neural network for graph of graphs link prediction"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "probabilistic similarity logic"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-3 (induced by source paper "drum end to end differentiable rule mining on knowledge graphs"):
  with members:
    - "variational graph recurrent neural networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "semi implicit graph variational auto encoders"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "graph embedding techniques applications and performance a survey"
    - "towards neural theorem proving at scale"

Connected through hyperedge HE-4 (induced by source paper "an overview of embedding models of entities and relationships for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "recurrent one hop predictions for reasoning over knowledge graphs"
    - "variational knowledge graph reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable and compositional relation learning by joint training with an autoencoder"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-5 (induced by source paper "convolutional 2d knowledge graph embeddings"):
  with members:
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "complex embeddings for simple link prediction"
    - "type constrained representation learning in knowledge graphs"
    - "deep residual learning for image recognition"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "knowledge base completion baselines strike back"):
  with members:
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "link prediction using embedded knowledge graphs"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "kblrn end to end learning of knowledge base representations with latent relational and numerical features"):
  with members:
    - "on multi relational link prediction with bilinear models"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "combining two and three way embeddings models for link prediction in knowledge bases"
    - "traversing knowledge graphs in vector space"
    - "adam a method for stochastic optimization"
    - "learning multi relational semantics using neural embedding models"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-8 (induced by source paper "on multi relational link prediction with bilinear models"):
  with members:
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "analogical inference for multi relational embeddings"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "transg a generative mixture model for knowledge graph embedding"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)