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: "learning k way d dimensional discrete code for compact embedding representations"
  Abstract: Embedding methods such as word embedding have become pillars for many applications containing discrete structures. Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols. Despite its simplicity, such approach yields number of parameters that grows linearly with the vocabulary size and can lead to overfitting. In this work we propose a much more compact K-way D-dimensional discrete encoding scheme to replace the "one-hot" encoding. In "KD encoding", each symbol is represented by a $D$-dimensional code, and each of its dimension has a cardinality of $K$. The final symbol embedding vector can be generated by composing the code embedding vectors. To learn the semantically meaningful code, we derive a relaxed discrete optimization technique based on stochastic gradient descent. By adopting the new coding system, the efficiency of parameterization can be significantly improved (from linear to logarithmic), and this can also mitigate the over-fitting problem. In our experiments with language modeling, the number of embedding parameters can be reduced by 97\% while achieving similar or better performance.

Connected through hyperedge HE-1 (induced by source paper "learning multi granular quantized embeddings for large vocab categorical features in recommender systems"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "differentiable product quantization for end to end embedding compression"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "self attentive sequential recommendation"
    - "adaptive input representations for neural language modeling"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-2 (induced by source paper "tensorized embedding layers for efficient model compression"):
  with members:
    - "a tensorized transformer for language modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "generalized tensor models for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"

Connected through hyperedge HE-3 (induced by source paper "learning compressed sentence representations for on device text processing"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "improving sentence representations with multi view frameworks"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-4 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "superbloom bloom filter meets transformer"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "next item recommendation with self attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hash embeddings for efficient word representations"
    - "attention is all you need"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-6 (induced by source paper "compositional embeddings using complementary partitions for memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensorized embedding layers for efficient model compression"
    - "on the convergence of adam and beyond"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "mars memory attention aware recommender system"
    - "on the dimensionality of word embedding"
    - "atrank an attention based user behavior modeling framework for recommendation"

Connected through hyperedge HE-7 (induced by source paper "doubly sparse sparse mixture of sparse experts for efficient softmax inference"):
  with members:
    - "learning to screen for fast softmax inference on large vocabulary neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "efficient softmax approximation for gpus"
    - "pointer sentinel mixture models"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "multi hot compact network embedding"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "cosine compressive network embedding on large scale information networks"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "a survey on network embedding"
    - "graphgan graph representation learning with generative adversarial nets"

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.LG(Machine Learning)