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: "harp hierarchical representation learning for networks"
  Abstract: We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e. local minima) which can pose problems to non-convex optimization. HARP works by finding a smaller graph which approximates the global structure of its input. This simplified graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. HARP is a general meta-strategy to improve all of the state-of-the-art neural algorithms for embedding graphs, including DeepWalk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP's hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on both classification tasks on real-world graphs such as DBLP, BlogCatalog, CiteSeer, and Arxiv, where we achieve a performance gain over the original implementations by up to 14% Macro F1.

Connected through hyperedge HE-1 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "large scale learnable graph convolutional networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "better understanding hierarchical visual relationship for image caption"):
  with members:
    - "selective attention based graph convolutional networks for aspect level sentiment classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "person re identification with deep similarity guided graph neural network"
    - "scaling neural machine translation"
    - "a comprehensive survey of deep learning for image captioning"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "hicore visual hierarchical context reasoning"):
  with members:
    - "connection sensitive attention u net for accurate retinal vessel segmentation"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "graph neural networks a review of methods and applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "iterative visual reasoning beyond convolutions"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-4 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank"):
  with members:
    - "adversarial training methods for network embedding"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "efficient algorithms for approximate single source personalized pagerank queries"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph embeddings via sparse transpose proximities"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"

Connected through hyperedge HE-5 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "graph attention auto encoders"):
  with members:
    - "universal transformers"
    - "detecting antagonistic and allied communities on social media"
    - "graph convolutional neural networks for web scale recommender systems"
    - "sentiment driven community profiling and detection on social media"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning graph representations with embedding propagation"

Connected through hyperedge HE-8 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding"):
  with members:
    - "network embedding on compression and learning"
    - "initialization for network embedding a graph partition approach"
    - "learning topological representation for networks via hierarchical sampling"
    - "label efficient semi supervised learning via graph filtering"
    - "graph neural networks a review of methods and applications"
    - "mile a multi level framework for scalable graph embedding"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph embedding techniques applications and performance a survey"

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)