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: "dynamic meta embeddings for improved sentence representations"
  Abstract: While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-2 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "deconstructing and reconstructing word embedding algorithms"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi task learning for sequence tagging an empirical study"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reconstruction of word embeddings from sub word parameters"
    - "compressing word embeddings via deep compositional code learning"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-4 (induced by source paper "argan attentive recurrent generative adversarial network for shadow detection and removal"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "direction aware spatial context features for shadow detection and removal"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "direction aware spatial context features for shadow detection"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-5 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "revealing the dark secrets of bert"
    - "linguistic knowledge and transferability of contextual representations"
    - "meta multi task learning for sequence modeling"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-6 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "sentence meta embeddings for unsupervised semantic textual similarity"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "improving sentence representations with multi view frameworks"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "angular based word meta embedding learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-8 (induced by source paper "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"):
  with members:
    - "policy gradients for contextual recommendations"
    - "lifelong domain word embedding via meta learning"
    - "meta learning a survey"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy optimization with model based explorations"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"

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)