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: "query chains learning to rank from implicit feedback"
  Abstract: This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.

Connected through hyperedge HE-1 (induced by source paper "semeval 2016 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "answer sequence learning with neural networks for answer selection in community question answering"

Connected through hyperedge HE-2 (induced by source paper "measuring the importance of user generated content to search engines"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "botornot a system to evaluate social bots"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "not at home on the range peer production and the urban rural divide"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"

Connected through hyperedge HE-3 (induced by source paper "practical user feedback driven internal search using online learning to rank"):
  with members:
    - "improved answer selection with pre trained word embeddings"
    - "reading wikipedia to answer open domain questions"
    - "a deep relevance matching model for ad hoc retrieval"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "rex explaining relationships between entity pairs"

Connected through hyperedge HE-5 (induced by source paper "influence of neighborhood on the preference of an item in ecommerce search"):
  with members:
    - "on application of learning to rank for e commerce search"
    - "learning to order things"

Connected through hyperedge HE-6 (induced by source paper "exploring the effect of an item s neighborhood on its sellability in ecommerce"):
  with members:
    - "on application of learning to rank for e commerce search"
    - "learning to order things"

Connected through hyperedge HE-7 (induced by source paper "the usage of web search for software engineering"):
  with members:
    - "interactions between health searchers and search engines"
    - "auditing search engines for differential satisfaction across demographics"

Connected through hyperedge HE-8 (induced by source paper "semeval 2015 task 3 answer selection in community question answering"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

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.LG(Machine Learning)