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: "modeling relation paths for representation learning of knowledge bases"
  Abstract: Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.

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 "mining commonsense facts from the physical world"):
  with members:
    - "joint detection and location of english puns"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "aligning cross lingual entities with multi aspect information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "n ary relation extraction using graph state lstm"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "automatic extraction of commonsense locatednear knowledge"

Connected through hyperedge HE-3 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "on evaluating embedding models for knowledge base completion"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"

Connected through hyperedge HE-4 (induced by source paper "relation embedding for personalised poi recommendation"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "translation based recommendation"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "knowledge graph based methods for record linkage"):
  with members:
    - "holographic embeddings of knowledge graphs"
    - "a hierarchical graphical model for record linkage"
    - "paris probabilistic alignment of relations instances and schema"

Connected through hyperedge HE-6 (induced by source paper "neural consciousness flow"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "relational deep reinforcement learning"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "dynamically pruned message passing networks for large scale knowledge graph reasoning"):
  with members:
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "how powerful are graph neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "non local neural networks"

Connected through hyperedge HE-8 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"

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.CL(Computation and Language)