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: "towards ai complete question answering a set of prerequisite toy tasks"
  Abstract: One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.

Connected through hyperedge HE-1 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-2 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "nail a general interactive fiction agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "interactive fiction games a colossal adventure"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "on the variance of the adaptive learning rate and beyond"
    - "learning to speak and act in a fantasy text adventure game"

Connected through hyperedge HE-3 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "memo a deep network for flexible combination of episodic memories"):
  with members:
    - "relational deep reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural networks for efficient inference"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "transformers as soft reasoners over language"):
  with members:
    - "differentiable reasoning on large knowledge bases and natural language"
    - "learning deep transformer models for machine translation"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "probing natural language inference models through semantic fragments"
    - "improving graph neural network representations of logical formulae with subgraph pooling"
    - "analysing mathematical reasoning abilities of neural models"
    - "establishing strong baselines for the new decade sequence tagging syntactic and semantic parsing with bert"
    - "quartz an open domain dataset of qualitative relationship questions"

Connected through hyperedge HE-6 (induced by source paper "plato dialogue system a flexible conversational ai research platform"):
  with members:
    - "moss end to end dialog system framework with modular supervision"
    - "collaborative multi agent dialogue model training via reinforcement learning"
    - "convlab multi domain end to end dialog system platform"
    - "huggingface s transformers state of the art natural language processing"
    - "nemo a toolkit for building ai applications using neural modules"
    - "adversarial learning of task oriented neural dialog models"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "detecting offensive content in open domain conversations using two stage semi supervision"

Connected through hyperedge HE-7 (induced by source paper "hypersched dynamic resource reallocation for model development on a deadline"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "an argument in favor of strong scaling for deep neural networks with small datasets"
    - "tiresias predicting security events through deep learning"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "tune a research platform for distributed model selection and training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "slaq quality driven scheduling for distributed machine learning"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (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"
    - "learning to reason with third order tensor products"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "attention is all you need"

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)