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: "improved semantic aware network embedding with fine grained word alignment"
  Abstract: Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. We propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce a word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. Results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "stochastic answer networks for natural language inference"
    - "network representation learning a survey"

Connected through hyperedge HE-2 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "pytorch biggraph a large scale graph embedding system"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "dyngem deep embedding method for dynamic graphs"
    - "diffusion maps for textual network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning over dynamic graphs"
    - "predicting multicellular function through multi layer tissue networks"

Connected through hyperedge HE-4 (induced by source paper "reasoning over semantic level graph for fact checking"):
  with members:
    - "towards debiasing fact verification models"
    - "adversarial attacks against fact extraction and verification"
    - "simple bert models for relation extraction and semantic role labeling"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "the fact extraction and verification fever shared task"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "a deep neural information fusion architecture for textual network embeddings"):
  with members:
    - "sac2vec information network representation with structure and content"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "language modeling with gated convolutional networks"
    - "a general framework for content enhanced network representation learning"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "embedding biomedical ontologies by jointly encoding network structure and textual node descriptors"):
  with members:
    - "abusive language detection with graph convolutional networks"
    - "deep relevance ranking using enhanced document query interactions"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "bag of tricks for efficient text classification"
    - "deep residual learning for image recognition"
    - "line large scale information network embedding"

Connected through hyperedge HE-7 (induced by source paper "diffusion maps for textual network embedding"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "learning generic sentence representations using convolutional neural networks"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "skip thought vectors"
    - "line large scale information network embedding"
    - "diffusion convolutional neural networks"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)