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: "on the equivalence of holographic and complex embeddings for link prediction"
  Abstract: We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al's holographic embedding and Trouillon et al.'s complex embedding. We first consider a spectral version of the holographic embedding, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting method reveals that it can be viewed as an instance of the complex embedding with certain constraints cast on the initial vectors upon training. Conversely, any complex embedding can be converted to an equivalent holographic embedding.

Connected through hyperedge HE-1 (induced by source paper "autokge searching scoring functions for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "searching for interaction functions in collaborative filtering"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "on evaluating embedding models for knowledge base completion"

Connected through hyperedge HE-2 (induced by source paper "relation embedding with dihedral group in knowledge graph"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-3 (induced by source paper "rotate knowledge graph embedding by relational rotation in complex space"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "end to end differentiable proving"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "binarized canonical polyadic decomposition for knowledge graph completion"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
    - "a survey on methods and theories of quantized neural networks"
    - "word2bits quantized word vectors"
    - "on multi relational link prediction with bilinear models"
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-5 (induced by source paper "binarized knowledge graph embeddings"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
    - "a survey on methods and theories of quantized neural networks"
    - "word2bits quantized word vectors"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "quaternion collaborative filtering for recommendation"):
  with members:
    - "deeprec an open source toolkit for deep learning based recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "next item recommendation with self attention"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"

Connected through hyperedge HE-7 (induced by source paper "mde multiple distance embeddings for link prediction in knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on multi relational link prediction with bilinear models"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "group representation theory for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "quaternion knowledge graph embedding"
    - "can graph neural networks help logic reasoning"
    - "probabilistic logic neural networks for reasoning"
    - "improving knowledge graph embedding using simple constraints"
    - "toruse knowledge graph embedding on a lie group"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

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)