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 to reason with third order tensor products"
  Abstract: We combine Recurrent Neural Networks with Tensor Product Representations to learn combinatorial representations of sequential data. This improves symbolic interpretation and systematic generalisation. Our architecture is trained end-to-end through gradient descent on a variety of simple natural language reasoning tasks, significantly outperforming the latest state-of-the-art models in single-task and all-tasks settings. We also augment a subset of the data such that training and test data exhibit large systematic differences and show that our approach generalises better than the previous state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "the tensor brain semantic decoding for perception and memory"):
  with members:
    - "reasoning on knowledge graphs with debate dynamics"
    - "improving visual relation detection using depth maps"
    - "learning by abstraction the neural state machine"
    - "context aware zero shot recognition"
    - "embedding models for episodic knowledge graphs"
    - "improving information extraction from images with learned semantic models"
    - "graph r cnn for scene graph generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "self attentive associative memory"):
  with members:
    - "neural stored program memory"
    - "learning to remember more with less memorization"
    - "metalearned neural memory"
    - "bilinear attention networks"
    - "relational recurrent neural networks"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "self assttentive associative memory"):
  with members:
    - "metalearned neural memory"
    - "universal transformers"
    - "attention is all you need"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (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-5 (induced by source paper "metalearned neural memory"):
  with members:
    - "contextual memory trees"
    - "learning attractor dynamics for generative memory"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "metalearning with hebbian fast weights"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "rapid adaptation with conditionally shifted neurons"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-6 (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"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-7 (induced by source paper "a perspective on objects and systematic generalization in model based rl"):
  with members:
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "an introduction to deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent world models facilitate policy evolution"
    - "ai safety gridworlds"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-8 (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"
    - "a survey on semantic parsing"
    - "question answering with grammatically interpretable representations"
    - "tensor product generation networks for deep nlp modeling"

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.LG(Machine Learning)