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: "a comparative study on regularization strategies for embedding based neural networks"
  Abstract: This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently applied or newly proposed regularization strategies, including penalizing weights (embeddings excluded), penalizing embeddings, re-embedding words, and dropout. We also emphasized on incremental hyperparameter tuning, and combining different regularizations. The results provide a picture on tuning hyperparameters for neural NLP models.

Connected through hyperedge HE-1 (induced by source paper "text classification with word embedding regularization and soft similarity measure"):
  with members:
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "compressing neural networks with the hashing trick"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "λopt learn to regularize recommender models in finer levels"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "entity embeddings of categorical variables"
    - "a generic coordinate descent framework for learning from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for sequences via learning to collocate"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "online updating of word representations for part of speech tagging"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "niser normalized item and session representations with graph neural networks"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "session based recommendation with graph neural network"
    - "ring loss convex feature normalization for face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "attention is all you need"
    - "neural attentive session based recommendation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "niser normalized item and session representations to handle popularity bias"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "session based recommendation with graph neural network"
    - "ring loss convex feature normalization for face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "attention is all you need"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-6 (induced by source paper "sequence to backward and forward sequences a content introducing approach to generative short text conversation"):
  with members:
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "chinese poetry generation with planning based neural network"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"
    - "backward and forward language modeling for constrained sentence generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "neural generative question answering"

Connected through hyperedge HE-7 (induced by source paper "how transferable are neural networks in nlp applications"):
  with members:
    - "transfer learning for speech and language processing"
    - "self adaptive hierarchical sentence model"
    - "natural language inference by tree based convolution and heuristic matching"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a large annotated corpus for learning natural language inference"
    - "discriminative neural sentence modeling by tree based convolution"
    - "how transferable are features in deep neural networks"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-8 (induced by source paper "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"):
  with members:
    - "the effects of hyperparameters on sgd training of neural networks"
    - "bayesian optimization of text representations"
    - "a primer on neural network models for natural language processing"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "practical recommendations for gradient based training of deep architectures"

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)