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: "attentive tensor product learning"
  Abstract: This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"

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"
    - "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"
    - "tensor product generation networks for deep nlp modeling"

Connected through hyperedge HE-3 (induced by source paper "tpsgtr neural symbolic tensor product scene graph triplet representation for image captioning"):
  with members:
    - "neural baby talk"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "tensor product generation networks for deep nlp modeling"
    - "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-4 (induced by source paper "question classification with deep contextualized transformer"):
  with members:
    - "multi level gated recurrent neural network for dialog act classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (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"
    - "question answering with grammatically interpretable representations"
    - "tensor product generation networks for deep nlp modeling"
    - "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.CL(Computation and Language)