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: "structural deep embedding for hyper networks"
  Abstract: Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep learning for learning graph representations"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "optimal transport for domain adaptation"
    - "deep residual learning for image recognition"
    - "role discovery in networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "word representations via gaussian embedding"

Connected through hyperedge HE-2 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"

Connected through hyperedge HE-3 (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-4 (induced by source paper "initialization for network embedding a graph partition approach"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "a tutorial on network embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-5 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "a dual framework for low rank tensor completion"
    - "efficient softmax approximation for gpus"
    - "a neural autoregressive approach to collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "hyper sagnn a self attention based graph neural network for hypergraphs"):
  with members:
    - "hypergraph convolution and hypergraph attention"
    - "hypergraph neural networks"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "deep hyperedges a framework for transductive and inductive learning on hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "hypergraph convolution and hypergraph attention"
    - "hypergraph neural networks"
    - "deep sets"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-8 (induced by source paper "representation learning for heterogeneous information networks via embedding events"):
  with members:
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "a survey of heterogeneous information network analysis"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "predicting social links for new users across aligned heterogeneous social 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.SI(Social and Information Networks)