human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "semantic guided and response times bounded top k similarity search over knowledge graphs"):
  Abstract: Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph $G_Q$, graph query finds subgraphs in a knowledge graph $G$ that exactly or approximately match $G_Q$. We face two challenges on graph query over a knowledge graph: (1) the structural gap between $G_Q$ and the predefined schema in $G$ causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic guided and response-time-bounded graph query to return top-k answers effectively and efficiently. We first leverage a knowledge graph embedding model to build the semantic graph $SG_Q$ for each $G_Q$. Then we define the path semantic similarity ($pss$) over $SG_Q$ to evaluate the match's quality. We propose an A* semantic search on $SG_Q$ to find top-k answers with the greatest $pss$ via a heuristic $pss$ estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user-specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency.

Includes members:
  Member "accelerating partial evaluation in distributed sparql query evaluation":
    also connected through other hyperedges:
      (none)
  Member "keyword search on rdf graphs a query graph assembly approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structured query construction via knowledge graph embedding")
  Member "an analytical study of large sparql query logs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "kbqa learning question answering over qa corpora and knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hhh an online medical chatbot system based on knowledge graph and hierarchical bi directional attention")
  Member "processing sparql queries over distributed rdf graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive low level storage of very large knowledge graphs")
  Member "connecting language and knowledge bases with embedding models for relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "querying knowledge graphs by example entity tuples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the ubiquity of large graphs and surprising challenges of graph processing extended survey")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

Question: Please tell me which class the source paper 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)