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: "hierarchical density order embeddings"
  Abstract: By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" correspond to broad distributions that encompass more specific words such as "animal" or "instrument". We introduce density order embeddings, which learn hierarchical representations through encapsulation of probability densities. In particular, we propose simple yet effective loss functions and distance metrics, as well as graph-based schemes to select negative samples to better learn hierarchical density representations. Our approach provides state-of-the-art performance on the WordNet hypernym relationship prediction task and the challenging HyperLex lexical entailment dataset -- while retaining a rich and interpretable density representation.

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"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "improved representation learning for predicting commonsense ontologies"
    - "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 "target oriented deformation of visual semantic embedding space"):
  with members:
    - "camp cross modal adaptive message passing for text image retrieval"
    - "visual semantic reasoning for image text matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "matching images and text with multi modal tensor fusion and re ranking"
    - "learning similarity conditions without explicit supervision"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "learning visual relation priors for image text matching and image captioning with neural scene graph generators"

Connected through hyperedge HE-3 (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"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "why we have switched from building full fledged taxonomies to simply detecting hypernymy relations"
    - "distributional inclusion vector embedding for unsupervised hypernymy detection"

Connected through hyperedge HE-4 (induced by source paper "path ranking with attention to type hierarchies"):
  with members:
    - "tucker tensor factorization for knowledge graph completion"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "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"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "listen attend and spell"

Connected through hyperedge HE-5 (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"
    - "poincar e embeddings for learning hierarchical representations"
    - "improved representation learning for predicting commonsense ontologies"
    - "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-6 (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"
    - "improved representation learning for predicting commonsense ontologies"
    - "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-7 (induced by source paper "poincar e glove hyperbolic word embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

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)