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: "simrank query rewriting through link analysis of the click graph"
  Abstract: We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced version of Simrank: one that exploits weights on click graph edges and another that exploits ``evidence.'' We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.

Connected through hyperedge HE-1 (induced by source paper "domain constrained advertising keyword generation"):
  with members:
    - "attention based hierarchical neural query suggestion"
    - "mojitalk generating emotional responses at scale"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "a term based methodology for query reformulation understanding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "dynamic discovery of type classes and relations in semantic web data"):
  with members:
    - "an efficient metric of automatic weight generation for properties in instance matching technique"
    - "axiomatic ranking of network role similarity"
    - "a fast general methodology for information theoretically optimal encodings of graphs"

Connected through hyperedge HE-3 (induced by source paper "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"):
  with members:
    - "optimized cost per click in taobao display advertising"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

Connected through hyperedge HE-4 (induced by source paper "simrank computation on uncertain graphs"):
  with members:
    - "axiomatic ranking of network role similarity"

Connected through hyperedge HE-5 (induced by source paper "representation independent proximity and similarity search"):
  with members:
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-6 (induced by source paper "graph based anomaly detection and description a survey"):
  with members:
    - "detecting change points in the large scale structure of evolving networks"
    - "deltacon a principled massive graph similarity function"
    - "discriminative probabilistic models for relational data"
    - "role dynamics fast mining of large dynamic networks"
    - "estimating the prevalence of deception in online review communities"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "netsimile a scalable approach to size independent network similarity"
    - "measuring tie strength in implicit social networks"

Connected through hyperedge HE-7 (induced by source paper "efficient simrank computation via linearization"):
  with members:
    - "similarity join size estimation using locality sensitive hashing"

Connected through hyperedge HE-8 (induced by source paper "finding information through integrated ad hoc socializing in the virtual and physical world"):
  with members:
    - "what s in a session tracking individual behavior on the web"

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.DL(Digital Libraries)