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: "quaternion knowledge graph embedding"
  Abstract: In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings. More specifically, quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are modelled as rotations in the quaternion space. The advantages of the proposed approach are: (1) Latent inter-dependencies (between all components) are aptly captured with Hamilton product, encouraging a more compact interaction between entities and relations; (2) Quaternions enable expressive rotation in four-dimensional space and have more degree of freedom than rotation in complex plane; (3) The proposed framework is a generalization of ComplEx on hypercomplex space while offering better geometrical interpretations, concurrently satisfying the key desiderata of relational representation learning (i.e., modeling symmetry, anti-symmetry and inversion). Experimental results demonstrate that our method achieves state-of-the-art performance on four well-established knowledge graph completion benchmarks.

Connected through hyperedge HE-1 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a continuously growing dataset of sentential paraphrases"

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"
    - "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"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "logicenn a neural based knowledge graphs embedding model with logical rules"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on multi relational link prediction with bilinear models"
    - "adversarial sets for regularising neural link predictors"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"

Connected through hyperedge HE-4 (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"
    - "can graph neural networks help logic reasoning"
    - "probabilistic logic neural networks for reasoning"
    - "improving knowledge graph embedding using simple constraints"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "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)