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: "name disambiguation in anonymized graphs using network embedding"
  Abstract: In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.

Connected through hyperedge HE-1 (induced by source paper "fondue a framework for node disambiguation using network embeddings"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "conna addressing name disambiguation on the fly"):
  with members:
    - "end to end neural entity linking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "deep reinforcement learning for mention ranking coreference models"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "text matching as image recognition"
    - "match srnn modeling the recursive matching structure with spatial rnn"

Connected through hyperedge HE-3 (induced by source paper "easing embedding learning by comprehensive transcription of heterogeneous information networks"):
  with members:
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "struc2vec learning node representations from structural identity"
    - "learning edge representations via low rank asymmetric projections"
    - "representation learning on graphs methods and applications"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "multi level network embedding with boosted low rank matrix approximation"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "visualizing large scale and high dimensional data"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-5 (induced by source paper "neural brane neural bayesian personalized ranking for attributed network embedding"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "learning graph representations with embedding propagation"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "robust actor critic contextual bandit for mobile health mhealth interventions"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "effective warm start for the online actor critic reinforcement learning based mhealth intervention"
    - "recurrent reinforcement learning a hybrid approach"
    - "median filtering is equivalent to sorting"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "learning domain sensitive and sentiment aware word embeddings"):
  with members:
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "neural semantic role labeling with dependency path embeddings"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "pair linking for collective entity disambiguation two could be better than all"):
  with members:
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "loopy belief propagation for approximate inference an empirical study"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

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.SI(Social and Information Networks)