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 representation learning for predicting commonsense ontologies"
  Abstract: Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical relation learning, with an aim towards improved performance on text data for commonsense knowledge representation. Our first model jointly learns ordering relations and non-hierarchical knowledge in the form of raw text. Our second extension exploits the partial order structure of the training data to find long-distance triplet constraints among embeddings which are poorly enforced by the pairwise training procedure. We find that both incorporating free text and augmented training constraints improve over the original order-embedding model and other strong baselines.

Connected through hyperedge HE-1 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on understanding knowledge graph representation"
    - "deep sets"
    - "embedding logical queries on knowledge graphs"
    - "hierarchical density order embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-2 (induced by source paper "inferring concept hierarchies from text corpora via hyperbolic embeddings"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "taskonomy disentangling task transfer learning"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "embedding text in hyperbolic spaces"
    - "hierarchical density order embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "why we have switched from building full fledged taxonomies to simply detecting hypernymy relations"

Connected through hyperedge HE-3 (induced by source paper "inducing hypernym relationships based on order theory"):
  with members:
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "hierarchical density order embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "order embeddings of images and language"

Connected through hyperedge HE-4 (induced by source paper "hypernym detection using strict partial order networks"):
  with members:
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "hierarchical density order embeddings"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "websets extracting sets of entities from the web using unsupervised information extraction"

Connected through hyperedge HE-5 (induced by source paper "hierarchical density order embeddings"):
  with members:
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "skip thought vectors"
    - "adam a method for stochastic optimization"
    - "word representations via gaussian embedding"
    - "efficient estimation of word representations in vector space"

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)