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: "tracking the world state with recurrent entity networks"
  Abstract: We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respond as is the case for a Memory Network (Sukhbaatar et al., 2015). Like a Neural Turing Machine or Differentiable Neural Computer (Graves et al., 2014; 2016) it maintains a fixed size memory and can learn to perform location and content-based read and write operations. However, unlike those models it has a simple parallel architecture in which several memory locations can be updated simultaneously. The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting. We also demonstrate that it can solve a reasoning task which requires a large number of supporting facts, which other methods are not able to solve, and can generalize past its training horizon. It can also be practically used on large scale datasets such as Children's Book Test, where it obtains competitive performance, reading the story in a single pass.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (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-3 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "context aware neural based dialog act classification on automatically generated transcriptions"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "probing for semantic classes diagnosing the meaning content of word embeddings"

Connected through hyperedge HE-4 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-5 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-6 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-7 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "modular networks learning to decompose neural computation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"

Connected through hyperedge HE-8 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"

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)