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: "a term based methodology for query reformulation understanding"
  Abstract: Key to any research involving session search is the understanding of how a user's queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user's information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user's motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.

Connected through hyperedge HE-1 (induced by source paper "context attentive document ranking and query suggestion"):
  with members:
    - "attention based hierarchical neural query suggestion"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "a click sequence model for web search"
    - "match tensor a deep relevance model for search"
    - "neural ranking models with weak supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-2 (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"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-3 (induced by source paper "learning to attend copy and generate for session based query suggestion"):
  with members:
    - "data augmentation for low resource neural machine translation"
    - "neural ranking models with weak supervision"
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-4 (induced by source paper "an extended relevance model for session search"):
  with members:
    - "lexical query modeling in session search"

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.IR(Information Retrieval)