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: "pytorch biggraph a large scale graph embedding system"
  Abstract: Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment. We demonstrate comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. We train and evaluate embeddings on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges.

Connected through hyperedge HE-1 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "pykg2vec a python library for knowledge graph embedding"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "a re evaluation of knowledge graph completion methods"
    - "hypernetwork knowledge graph embeddings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "hygcn a gcn accelerator with hybrid architecture"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"
    - "adaptive sampling towards fast graph representation learning"

Connected through hyperedge HE-3 (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"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "are abstracts enough for hypothesis generation"
    - "attention is all you need"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-4 (induced by source paper "agl a scalable system for industrial purpose graph machine learning"):
  with members:
    - "dsslp a distributed framework for semi supervised link prediction"
    - "heterogeneous graph neural networks for malicious account detection"
    - "fast graph representation learning with pytorch geometric"
    - "graph convolutional neural networks for web scale recommender systems"
    - "sentence state lstm for text representation"
    - "predicting multicellular function through multi layer tissue networks"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-5 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "poised spotting twitter spam off the beaten paths"
    - "deep learning for hate speech detection in tweets"
    - "attention is all you need"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-6 (induced by source paper "characterizing and understanding gcns on gpu"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "how powerful are graph neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-7 (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"
    - "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"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-8 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "relational inductive biases deep learning and graph 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.LG(Machine Learning)