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: "efficient vector representation for documents through corruption"
  Abstract: We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.

Connected through hyperedge HE-1 (induced by source paper "towards deep federated defenses against malware in cloud ecosystems"):
  with members:
    - "how secure is your iot network"
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "deep hyperedges a framework for transductive and inductive learning on hypergraphs"
    - "multi task deep neural networks for natural language understanding"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "hypergraph convolution and hypergraph attention"
    - "what does bert look at an analysis of bert s attention"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "encouraging paragraph embeddings to remember sentence identity improves classification"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-3 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "probabilistic fasttext for multi sense word embeddings"
    - "ltsg latent topical skip gram for mutually learning topic model and vector representations"

Connected through hyperedge HE-4 (induced by source paper "hierarchical document encoder for parallel corpus mining"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "word mover s embedding from word2vec to document embedding"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "attention is all you need"
    - "exploiting cross sentence context for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "multimodal deep learning for mental disorders prediction from audio speech samples"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multi attention recurrent network for human communication comprehension"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-6 (induced by source paper "snu_ids at semeval 2018 task 12 sentence encoder with contextualized vectors for argument reasoning comprehension"):
  with members:
    - "deep contextualized word representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "question answering through transfer learning from large fine grained supervision data"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "how transferable are neural networks in nlp applications"

Connected through hyperedge HE-7 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"
    - "siamese cbow optimizing word embeddings for sentence representations"

Connected through hyperedge HE-8 (induced by source paper "on the importance of attention in meta learning for few shot text classification"):
  with members:
    - "learning to optimize neural nets"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "meta sgd learning to learn quickly for few shot learning"
    - "a structured self attentive sentence embedding"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)