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: "querying knowledge graphs by example entity tuples"
  Abstract: We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over simple tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, GQBE (Graph Query By Example), automatically derives a weighted hidden maximal query graph based on input query tuples, to capture a user's query intent. It efficiently finds and ranks the top approximate answer tuples. For fast query processing, GQBE only partially evaluates query graphs. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

Connected through hyperedge HE-1 (induced by source paper "the ubiquity of large graphs and surprising challenges of graph processing extended survey"):
  with members:
    - "graph based preconditioning conjugate gradient algorithm for n 1 contingency analysis"
    - "g core a core for future graph query languages"
    - "foundations of modern query languages for graph databases"
    - "the gremlin graph traversal machine and language invited talk"
    - "graphvista interactive exploration of large graphs"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-2 (induced by source paper "automatic table completion using knowledge base"):
  with members:
    - "table to text describing table region with natural language"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "finding patterns in a knowledge base using keywords to compose table answers"

Connected through hyperedge HE-3 (induced by source paper "structure preserving community in a multilayer network definition detection and analysis"):
  with members:
    - "generic multilayer network data analysis with the fusion of content and structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "organizational chart inference"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-4 (induced by source paper "context aware deep model for entity recommendation in search engine at alibaba"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "adaptive sampled softmax with kernel based sampling"
    - "node2vec scalable feature learning for networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "semantic guided and response times bounded top k similarity search over knowledge graphs"):
  with members:
    - "accelerating partial evaluation in distributed sparql query evaluation"
    - "keyword search on rdf graphs a query graph assembly approach"
    - "an analytical study of large sparql query logs"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "processing sparql queries over distributed rdf graphs"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-6 (induced by source paper "example driven query intent discovery abductive reasoning using semantic similarity"):
  with members:
    - "learning from positive and unlabeled data a survey"
    - "the complexity of reverse engineering problems for conjunctive queries"
    - "causes for query answers from databases datalog abduction view updates and integrity constraints"
    - "compositional vector space models for knowledge base completion"
    - "learning and verifying quantified boolean queries by example"
    - "coherent integration of databases by abductive logic programming"

Connected through hyperedge HE-7 (induced by source paper "making a case for mlns for data driven analysis modeling efficiency and versatility"):
  with members:
    - "structure preserving community in a multilayer network definition detection and analysis"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "a survey of heterogeneous information network analysis"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "grease a generative model for relevance search over knowledge graphs"):
  with members:
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "supervised random walks predicting and recommending links in social 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.DB(Databases)