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: "basic reasoning with tensor product representations"
  Abstract: In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)). After an initial brief synopsis of TPRs (Section 0), we begin with particular examples of inference with TPRs in the 'bAbI' question-answering task of Weston et al. (2015) (Section 1). We then present a simplification of the general analysis that suffices for the bAbI task (Section 2). Finally, we lay out the general treatment of inference over TPRs (Section 3). We also show the simplification in Section 2 derives the inference methods described in Lee et al. (2016); this shows how the simple methods of Lee et al. (2016) can be formally extended to more general reasoning tasks.

Connected through hyperedge HE-1 (induced by source paper "uw bhi at mediqa 2019 an analysis of representation methods for medical natural language inference"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "syntactic scaffolds for semantic structures"
    - "allennlp a deep semantic natural language processing platform"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "natural to formal language generation using tensor product representations"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "ain t nobody got time for coding structure aware program synthesis from natural language"
    - "attentive tensor product learning"
    - "neural approaches to conversational ai"
    - "neural program search solving programming tasks from description and examples"
    - "learning to reason with third order tensor products"
    - "a survey on semantic parsing"
    - "question answering with grammatically interpretable representations"

Connected through hyperedge HE-3 (induced by source paper "learning to reason with third order tensor products"):
  with members:
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "attentive tensor product learning for language generation and grammar parsing"
    - "on the state of the art of evaluation in neural language models"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "tensor train recurrent neural networks for video classification"
    - "long term forecasting using higher order tensor rnns"
    - "learning to generalize to new compositions in image understanding"
    - "scaling memory augmented neural networks with sparse reads and writes"

Connected through hyperedge HE-4 (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"
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "adversarial sets for regularising neural link predictors"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "complex embeddings for simple link prediction"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-5 (induced by source paper "reasoning in vector space an exploratory study of question answering"):
  with members:
    - "end to end memory networks"
    - "towards neural network based reasoning"
    - "ask me anything dynamic memory networks for natural language processing"
    - "question answering with subgraph embeddings"
    - "distributed representations of words and phrases and their compositionality"

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.AI(Artificial Intelligence)