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: "a survey of heterogeneous information network analysis"
  Abstract: Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

Connected through hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "hgat hierarchical graph attention network for fake news detection"):
  with members:
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "embedding logical queries on knowledge graphs"
    - "how powerful are graph neural networks"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "automatic detection of fake news"
    - "a stylometric inquiry into hyperpartisan and fake news"

Connected through hyperedge HE-3 (induced by source paper "supervised learning on relational databases with graph neural networks"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "tabnet attentive interpretable tabular learning"
    - "relational pooling for graph representations"
    - "neural feature learning from relational database"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks"):
  with members:
    - "coupled deep learning for heterogeneous face recognition"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "information diffusion in social networks in two phases"
    - "revisiting semi supervised learning with graph embeddings"
    - "spreading processes in multilayer networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "agatha automatic graph mining and transformer based hypothesis generation approach"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "pytorch biggraph a large scale graph embedding system"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "are abstracts enough for hypothesis generation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "measuring diversity in heterogeneous information networks"):
  with members:
    - "exploring author gender in book rating and recommendation"
    - "heterogeneous information network embedding for recommendation"
    - "measuring online social bubbles"
    - "renyi divergence and kullback leibler divergence"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-7 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-8 (induced by source paper "learning graph embedding with adversarial training methods"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "a comprehensive survey on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "attributed social network embedding"
    - "semi supervised learning on graphs 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.SI(Social and Information Networks)