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: "rex explaining relationships between entity pairs"
  Abstract: Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft, and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various studies have been conducted on how to leverage them to answer entity seeking queries. Meanwhile, in a complementary direction, analyses over the query logs have enabled researchers to identify entity pairs that are statistically correlated. Such entity relationships are then presented to search users through the "related searches" feature in modern search engines. However, entity relationships thus discovered can often be "puzzling" to the users because why the entities are connected is often indescribable. In this paper, we propose a novel problem called "entity relationship explanation", which seeks to explain why a pair of entities are connected, and solve this challenging problem by integrating the above two complementary approaches, i.e., we leverage the knowledge base to "explain" the connections discovered between entity pairs. More specifically, we present REX, a system that takes a pair of entities in a given knowledge base as input and efficiently identifies a ranked list of relationship explanations. We formally define relationship explanations and analyze their desirable properties. Furthermore, we design and implement algorithms to efficiently enumerate and rank all relationship explanations based on multiple measures of "interestingness." We perform extensive experiments over real web-scale data gathered from DBpedia and a commercial search engine, demonstrating the efficiency and scalability of REX. We also perform user studies to corroborate the effectiveness of explanations generated by REX.

Connected through hyperedge HE-1 (induced by source paper "relaxing relationship queries on graph data"):
  with members:
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-2 (induced by source paper "fairy a framework for understanding relationships between users actions and their social feeds"):
  with members:
    - "myadchoices bringing transparency and control to online advertising"
    - "xray enhancing the web s transparency with differential correlation"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "analyzing evolving stories in news articles"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised contextualization of knowledge graph facts"):
  with members:
    - "neural ranking models with weak supervision"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "neural text generation from structured data with application to the biography domain"
    - "tensorflow a system for large scale machine learning"
    - "traversing knowledge graphs in vector space"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "tell me why is it so explaining knowledge graph relationships by finding descriptive support passages"):
  with members:
    - "natural language generation enhances human decision making with uncertain information"
    - "timemachine timeline generation for knowledge base entities"

Connected through hyperedge HE-6 (induced by source paper "multimodal storytelling via generative adversarial imitation learning"):
  with members:
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "generative adversarial imitation learning"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"
    - "text understanding from scratch"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-7 (induced by source paper "conditional reliability in uncertain graphs"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "injecting uncertainty in graphs for identity obfuscation"

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)