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: "relnet end to end modeling of entities relations"
  Abstract: We introduce RelNet: a new model for relational reasoning. RelNet is a memory augmented neural network which models entities as abstract memory slots and is equipped with an additional relational memory which models relations between all memory pairs. The model thus builds an abstract knowledge graph on the entities and relations present in a document which can then be used to answer questions about the document. It is trained end-to-end: only supervision to the model is in the form of correct answers to the questions. We test the model on the 20 bAbI question-answering tasks with 10k examples per task and find that it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of the 20 tasks.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "what do entity centric models learn insights from entity linking in multi party dialogue"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "amore upf at semeval 2018 task 4 bilstm with entity library"
    - "evaluating theory of mind in question answering"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "improving coreference resolution by learning entity level distributed representations"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "learning global features for coreference resolution"

Connected through hyperedge HE-3 (induced by source paper "learning in text streams discovery and disambiguation of entity and relation instances"):
  with members:
    - "one for all towards language independent named entity linking"
    - "a neural knowledge language model"
    - "dynamic entity representations in neural language models"
    - "squad 100 000 questions for machine comprehension of text"
    - "tracking the world state with recurrent entity networks"
    - "neural architectures for named entity recognition"
    - "exploring the limits of language modeling"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-4 (induced by source paper "modeling financial analysts decision making via the pragmatics and semantics of earnings calls"):
  with members:
    - "attentive interaction model modeling changes in view in argumentation"
    - "asking too much the rhetorical role of questions in political discourse"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "enriching visual with verbal explanations for relational concepts combining lime with aleph"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "detecting unseen visual relations using analogies"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "a simple neural network module for relational reasoning"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-7 (induced by source paper "relational dynamic memory networks"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "attentional multilabel learning over graphs a message passing approach"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "a deep tree based model for software defect prediction"

Connected through hyperedge HE-8 (induced by source paper "open vocabulary learning on source code with a graph structured cache"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "a general path based representation for predicting program properties"
    - "generative code modeling with graphs"
    - "code completion with neural attention and pointer networks"
    - "relational inductive biases deep learning and graph networks"
    - "tree to tree neural networks for program translation"
    - "graph memory networks for molecular activity prediction"
    - "object oriented neural programming oonp for document understanding"

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)