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: "task guided and path augmented heterogeneous network embedding for author identification"
  Abstract: In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the problem, which can automatically represent nodes into lower dimensional feature vectors. However, there are two major limitations in recent studies on network embedding: (1) they are usually general-purpose embedding methods, which are independent of the specific tasks; and (2) most of these approaches can only deal with homogeneous networks, where the heterogeneity of the network is ignored. Hence, challenges faced here are two folds: (1) how to embed the network under the guidance of the author identification task, and (2) how to select the best type of information due to the heterogeneity of the network. #R##N#To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model. In our model, nodes are first embedded as vectors in latent feature space. Embeddings are then shared and jointly trained according to task-specific and network-general objectives. We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. Our experiments demonstrate that by using path-augmented network embedding with task guidance, our model can obtain significantly better accuracy at identifying the true authors comparing to existing methods.

Connected through hyperedge HE-1 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "graph neural networks for social recommendation"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous graph attention network"):
  with members:
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "embedding logical queries on knowledge graphs"
    - "graph embedding techniques applications and performance a survey"
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "attention is all you need"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-4 (induced by source paper "neural embedding propagation on heterogeneous networks"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "enhanced network embeddings via exploiting edge labels"
    - "graph edit distance computation via graph neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "representation learning on graphs methods and applications"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-6 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-7 (induced by source paper "dynamic network embeddings for network evolution analysis"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "struc2vec learning node representations from structural identity"
    - "motifs in temporal networks"
    - "timers error bounded svd restart on dynamic networks"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "adversarial network embedding"

Connected through hyperedge HE-8 (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"
    - "name disambiguation in anonymized graphs using network embedding"
    - "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"

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)