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: "deeppath a reinforcement learning method for knowledge graph reasoning"
  Abstract: We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.

Connected through hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "entity context and relational paths for knowledge graph completion"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "coke contextualized knowledge graph embedding"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "position aware graph neural networks"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "rule guided compositional representation learning on knowledge graphs"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "learning hierarchy aware knowledge graph embeddings for link prediction"
    - "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"
    - "tucker tensor factorization for knowledge graph completion"
    - "composition based multi relational graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning"):
  with members:
    - "graph few shot learning via knowledge transfer"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "top k off policy correction for a reinforce recommender system"
    - "deep reinforcement learning based recommendation with explicit user item interactions modeling"
    - "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 "inductive relation prediction by subgraph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "link prediction based on graph neural networks"
    - "logic attention based neighborhood aggregation for inductive knowledge graph embedding"
    - "how powerful are graph neural networks"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "debate dynamics for human comprehensible fact checking on knowledge graphs"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "convolutional 2d knowledge graph embeddings"
    - "why should i trust you explaining the predictions of any classifier"
    - "complex embeddings for simple link prediction"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-8 (induced by source paper "reasoning on knowledge graphs with debate dynamics"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "convolutional 2d knowledge graph embeddings"
    - "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.CL(Computation and Language)