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: "ssp semantic space projection for knowledge graph embedding with text descriptions"
  Abstract: Knowledge representation is an important, long-history topic in AI, and there have been a large amount of work for knowledge graph embedding which projects symbolic entities and relations into low-dimensional, real-valued vector space. However, most embedding methods merely concentrate on data fitting and ignore the explicit semantic expression, leading to uninterpretable representations. Thus, traditional embedding methods have limited potentials for many applications such as question answering, and entity classification. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments justify our model outperforms other state-of-the-art baselines substantially.

Connected through hyperedge HE-1 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "on the role of conceptualization in commonsense knowledge graph construction"):
  with members:
    - "kg bert bert for knowledge graph completion"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "aser a large scale eventuality knowledge graph"
    - "roberta a robustly optimized bert pretraining approach"
    - "a simple method for commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "the keen universe an ecosystem for knowledge graph embeddings with a focus on reproducibility and transferability"):
  with members:
    - "el embeddings geometric construction of models for the description logic el"
    - "leveraging knowledge graph embedding techniques for industry 4 0 use cases"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-5 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-6 (induced by source paper "kg bert bert for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "ernie enhanced language representation with informative entities"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "dolores deep contextualized knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-8 (induced by source paper "incorporating joint embeddings into goal oriented dialogues with multi task learning"):
  with members:
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "convolutional 2d knowledge graph embeddings"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"

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)