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: "variational knowledge graph reasoning"
  Abstract: Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (\textsc{Diva}) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, \textsc{Diva} is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.

Connected through hyperedge HE-1 (induced by source paper "dynamically pruned message passing networks for large scale knowledge graph reasoning"):
  with members:
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "text modeling with syntax aware variational autoencoders"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "syntax directed variational autoencoder for structured data"
    - "dirichlet variational autoencoder for text modeling"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-3 (induced by source paper "dykgchat benchmarking dialogue generation grounding on dynamic knowledge graphs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "key value retrieval networks for task oriented dialogue"
    - "a knowledge grounded neural conversation model"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "cognitive knowledge graph reasoning for one shot relational learning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "meta learning for low resource neural machine translation"
    - "relational inductive biases deep learning and graph networks"
    - "one shot relational learning for knowledge graphs"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-5 (induced by source paper "uhop an unrestricted hop relation extraction framework for knowledge based question answering"):
  with members:
    - "knowledge base relation detection via multi view matching"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "an attention based word level interaction model relation detection for knowledge base question answering"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "improved neural relation detection for knowledge base question answering"
    - "variational reasoning for question answering with knowledge graph"

Connected through hyperedge HE-6 (induced by source paper "adapting meta knowledge graph information for multi hop reasoning over few shot relations"):
  with members:
    - "cognitive graph for multi hop reading comprehension at scale"
    - "dynamically fused graph network for multi hop reasoning"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "one shot relational learning for knowledge graphs"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-7 (induced by source paper "meta reasoning over knowledge graphs"):
  with members:
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "one shot relational learning for knowledge graphs"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "improved neural relation detection for knowledge base 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"

Connected through hyperedge HE-8 (induced by source paper "a variational approach to weakly supervised document level multi aspect sentiment classification"):
  with members:
    - "weakly supervised neural text classification"
    - "variational reasoning for question answering with knowledge graph"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "aspect level sentiment classification with deep memory network"
    - "rationalizing neural predictions"
    - "tensorflow a system for large scale machine learning"

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)