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: "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
  Abstract: Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size. This makes them difficult to train, due to the limited memory of graphical processing units, and difficult to deploy on mobile devices with limited hardware. To address these difficulties, we propose Bloom embeddings, a compression technique that can be applied to the input and output of neural network models dealing with sparse high-dimensional binary-coded instances. Bloom embeddings are computationally efficient, and do not seriously compromise the accuracy of the model up to 1/5 compression ratios. In some cases, they even improve over the original accuracy, with relative increases up to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4 alternative methods, obtaining favorable results. We also discuss a number of further advantages of Bloom embeddings, such as 'on-the-fly' constant-time operation, zero or marginal space requirements, training time speedups, or the fact that they do not require any change to the core model architecture or training configuration.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records"):
  with members:
    - "secnlp a survey of embeddings in clinical natural language processing"
    - "bpe dropout simple and effective subword regularization"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "named entity recognition in electronic health records using transfer learning bootstrapped neural networks"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "not enough data deep learning to the rescue"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-3 (induced by source paper "learning contextualized document representations for healthcare answer retrieval"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning dense representations for entity retrieval"
    - "a question entailment approach to question answering"
    - "pubmedqa a dataset for biomedical research question answering"
    - "zero shot entity linking by reading entity descriptions"
    - "sector a neural model for coherent topic segmentation and classification"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "anmm ranking short answer texts with attention based neural matching model"

Connected through hyperedge HE-4 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "do convolutional networks need to be deep for text classification"
    - "benchmark for complex answer retrieval"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "very deep convolutional networks for text classification"

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"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-6 (induced by source paper "graph dna deep neighborhood aware graph encoding for collaborative filtering"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "pu learning for matrix completion"

Connected through hyperedge HE-7 (induced by source paper "how robust is movielens a dataset analysis for recommender systems"):
  with members:
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"

Connected through hyperedge HE-8 (induced by source paper "efficient mixture model for clustering of sparse high dimensional binary data"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "semi supervised cross entropy clustering with information bottleneck constraint"
    - "hyperplane clustering via dual principal component pursuit"
    - "innovation pursuit a new approach to subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "why should i trust you explaining the predictions of any classifier"
    - "cross entropy clustering"
    - "entropy of the mixture of sources and entropy dimension"

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)