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: "learning multi relational semantics using neural embedding models"
  Abstract: In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the design of a simple embedding model that achieves the new state-of-the-art performance on a popular knowledge base completion task evaluated on Freebase.

Connected through hyperedge HE-1 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both"):
  with members:
    - "logic rules powered knowledge graph embedding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "towards data poisoning attack against knowledge graph embedding"):
  with members:
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "data poisoning attack against unsupervised node embedding methods"
    - "semi supervised classification with graph convolutional networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "transa an adaptive approach for knowledge graph embedding"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "data poisoning attack against knowledge graph embedding"):
  with members:
    - "adversarial attack on graph structured data"
    - "data poisoning attack against unsupervised node embedding methods"
    - "certified defenses for data poisoning attacks"
    - "semi supervised classification with graph convolutional networks"
    - "adversarial examples in the physical world"
    - "transa an adaptive approach for knowledge graph embedding"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-4 (induced by source paper "learning sequence encoders for temporal knowledge graph completion"):
  with members:
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "knowledge base completion baselines strike back"
    - "character aware neural language models"
    - "traversing knowledge graphs in vector space"
    - "predicting the co evolution of event and knowledge graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "answering visual relational queries in web extracted knowledge graphs"):
  with members:
    - "embedding multimodal relational data for knowledge base completion"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "learning to disambiguate by asking discriminative questions"
    - "deep metric learning with angular loss"
    - "a neural knowledge language model"
    - "knowledge base completion baselines strike back"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "deep metric learning via facility location"

Connected through hyperedge HE-6 (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"
    - "discriminative gaifman models"
    - "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"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-7 (induced by source paper "gated attention readers for text comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "tweet2vec character based distributed representations for social media"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "broad context language modeling as reading comprehension"
    - "text understanding with the attention sum reader network"
    - "the lambada dataset word prediction requiring a broad discourse context"

Connected through hyperedge HE-8 (induced by source paper "combining two and three way embeddings models for link prediction in knowledge bases"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"

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)