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: "transa an adaptive approach for knowledge graph embedding"
  Abstract: Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space. Among these attempts, translation-based methods build entity and relation vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods, translation-based methods also suffer from the oversimplified loss metric, and are not competitive enough to model various and complex entities/relations in knowledge bases. To address this issue, we propose \textbf{TransA}, an adaptive metric approach for embedding, utilizing the metric learning ideas to provide a more flexible embedding method. Experiments are conducted on the benchmark datasets and our proposed method makes significant and consistent improvements over the state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "short text classification via knowledge powered attention with similarity matrix based cnn"):
  with members:
    - "deep short text classification with knowledge powered attention"
    - "cn probase a data driven approach for large scale chinese taxonomy construction"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "multilingual hierarchical attention networks for document classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-3 (induced by source paper "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"):
  with members:
    - "on multi relational link prediction with bilinear models"
    - "translation based recommendation"
    - "convolutional 2d knowledge graph embeddings"
    - "on complex valued convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-4 (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"
    - "learning multi relational semantics using neural embedding models"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-5 (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"
    - "learning multi relational semantics using neural embedding models"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-6 (induced by source paper "evaluating the effectiveness of margin parameter when learning knowledge embedding representation for domain specific multi relational categorized data"):
  with members:
    - "incorporating gan for negative sampling in knowledge representation learning"
    - "knowledge base completion baselines strike back"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-7 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-8 (induced by source paper "revisiting simple neural networks for learning representations of knowledge graphs"):
  with members:
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "transg a generative mixture model for knowledge graph embedding"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"

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)