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: "word representations via gaussian embedding"
  Abstract: Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision boundaries. This paper advocates for density-based distributed embeddings and presents a method for learning representations in the space of Gaussian distributions. We compare performance on various word embedding benchmarks, investigate the ability of these embeddings to model entailment and other asymmetric relationships, and explore novel properties of the representation.

Connected through hyperedge HE-1 (induced by source paper "an inductive bias for distances neural nets that respect the triangle inequality"):
  with members:
    - "a projection method for metric constrained optimization"
    - "sorting out lipschitz function approximation"
    - "on evaluation of embodied navigation agents"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "prototypical networks for few shot learning"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "node2vec scalable feature learning for networks"
    - "input convex neural networks"

Connected through hyperedge HE-2 (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"
    - "improved representation learning for predicting commonsense ontologies"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for learning graph representations"):
  with members:
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"
    - "optimal transport for domain adaptation"
    - "deep residual learning for image recognition"
    - "role discovery in networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "probabilistic face embeddings"):
  with members:
    - "striking the right balance with uncertainty"
    - "video face recognition component wise feature aggregation network c fan"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-7 (induced by source paper "correlation coefficients and semantic textual similarity"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-8 (induced by source paper "spherical text embedding"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "embedding text in hyperbolic spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "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)