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: "complex and holographic embeddings of knowledge graphs a comparison"
  Abstract: Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution. In this short paper, we are providing a comparison of two state-of-the-art knowledge graph embeddings for which their equivalence has recently been established, i.e., ComplEx and HolE [Nickel, Rosasco, and Poggio, 2016; Trouillon et al., 2016; Hayashi and Shimbo, 2017]. First, we briefly review both models and discuss how their scoring functions are equivalent. We then analyze the discrepancy of results reported in the original articles, and show experimentally that they are likely due to the use of different loss functions. In further experiments, we evaluate the ability of both models to embed symmetric and antisymmetric patterns. Finally, we discuss advantages and disadvantages of both models and under which conditions one would be preferable to the other.

Connected through hyperedge HE-1 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "quaternion knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "quaternion convolutional neural networks for heterogeneous image processing"
    - "knowledge graph embedding with entity neighbors and deep memory network"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "deep quaternion networks"
    - "toruse knowledge graph embedding on a lie group"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "a relational tucker decomposition for multi relational link prediction"):
  with members:
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "on multi relational link prediction with bilinear models"
    - "convolutional 2d knowledge graph embeddings"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "wikidatasets standardized sub graphs from wikidata"):
  with members:
    - "pytorch biggraph a large scale graph embedding system"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "knowledge graph embedding with multiple relation projections"):
  with members:
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "a neural knowledge language model"
    - "convolutional 2d knowledge graph embeddings"
    - "key value memory networks for directly reading documents"
    - "the more you know using knowledge graphs for image classification"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "large scale simple question answering with memory networks"

Connected through hyperedge HE-6 (induced by source paper "deep complex networks"):
  with members:
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "conditional image generation with pixelcnn decoders"
    - "recurrent highway networks"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "on inductive abilities of latent factor models for relational learning"):
  with members:
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "knowledge graph completion via complex tensor factorization"
    - "adversarial sets for regularising neural link predictors"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "basic reasoning with tensor product representations"
    - "complex embeddings for simple link prediction"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-8 (induced by source paper "on multi relational link prediction with bilinear models"):
  with members:
    - "analogical inference for multi relational embeddings"
    - "discriminative gaifman models"
    - "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)