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: "tensor product generation networks for deep nlp modeling"
  Abstract: We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks. A network architecture --- the Tensor Product Generation Network (TPGN) --- is proposed which is capable in principle of carrying out TPR computation, but which uses unconstrained deep learning to design its internal representations. Instantiated in a model for image-caption generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset. The TPR-capable structure enables interpretation of internal representations and operations, which prove to contain considerable grammatical content. Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation.

Connected through hyperedge HE-1 (induced by source paper "hubert untangles bert to improve transfer across nlp tasks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "semantics aware bert for language understanding"
    - "are sixteen heads really better than one"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "unifying question answering text classification and regression via span extraction"

Connected through hyperedge HE-2 (induced by source paper "a simple recurrent unit with reduced tensor product representations"):
  with members:
    - "correlating neural and symbolic representations of language"
    - "dynamic evaluation of transformer language models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "frage frequency agnostic word representation"
    - "deep contextualized word representations"
    - "can neural networks understand logical entailment"
    - "learning to reason with third order tensor products"
    - "glomo unsupervisedly learned relational graphs as transferable representations"

Connected through hyperedge HE-3 (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-4 (induced by source paper "tpsgtr neural symbolic tensor product scene graph triplet representation for image captioning"):
  with members:
    - "neural baby talk"
    - "attentive tensor product learning"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "semantic compositional networks for visual captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-5 (induced by source paper "learning distributed representations of symbolic structure using binding and unbinding operations"):
  with members:
    - "dynamic evaluation of transformer language models"
    - "hyperbolic attention networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "frage frequency agnostic word representation"
    - "relational recurrent neural networks"
    - "deep contextualized word representations"
    - "embedding logical queries on knowledge graphs"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-6 (induced by source paper "rnns implicitly implement tensor product representations"):
  with members:
    - "assessing composition in sentence vector representations"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "learning distributed representations of symbolic structure using binding and unbinding operations"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reason with third order tensor products"
    - "tree to tree neural networks for program translation"
    - "evaluating compositionality in sentence embeddings"

Connected through hyperedge HE-7 (induced by source paper "feature fusion effects of tensor product representation on de compositional network for caption generation for images"):
  with members:
    - "neural baby talk"
    - "attentive tensor product learning"
    - "question answering with grammatically interpretable representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "semantic compositional networks for visual captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"
    - "encode review and decode reviewer module for caption generation"

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.CV(Computer Vision and Pattern Recognition)