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: "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
  Abstract: Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.

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 "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-3 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "passage re ranking with bert"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multi hop knowledge graph reasoning with reward shaping"

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 "realistic re evaluation of knowledge graph completion methods an experimental study"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "on evaluating embedding models for knowledge base completion"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-6 (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"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "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-7 (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"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-8 (induced by source paper "neural consciousness flow"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "relational deep reinforcement learning"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"

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)