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: "entity embeddings with conceptual subspaces as a basis for plausible reasoning"
  Abstract: Conceptual spaces are geometric representations of conceptual knowledge, in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features. While conceptual spaces enable elegant models of various cognitive phenomena, the lack of automated methods for constructing such representations have so far limited their application in artificial intelligence. To address this issue, we propose a method which learns a vector-space embedding of entities from Wikipedia and constrains this embedding such that entities of the same semantic type are located in some lower-dimensional subspace. We experimentally demonstrate the usefulness of these subspaces as (approximate) conceptual space representations by showing, among others, that important features can be modelled as directions and that natural properties tend to correspond to convex regions.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-2 (induced by source paper "finding interpretable concept spaces in node embeddings using knowledge bases"):
  with members:
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "node representation learning for directed graphs"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "modelling semantic categories using conceptual neighborhood"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "learning conceptual space representations of interrelated concepts"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "nonparametric spherical topic modeling with word embeddings"

Connected through hyperedge HE-4 (induced by source paper "new embedded representations and evaluation protocols for inferring transitive relations"):
  with members:
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "learning distributed representations of texts and entities from knowledge base"
    - "finer grained entity typing with typenet"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "poincar e embeddings for learning hierarchical representations"
    - "corpus level fine grained entity typing using contextual information"
    - "an attentive neural architecture for fine grained entity type classification"
    - "complex embeddings for simple link prediction"

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.AI(Artificial Intelligence)