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: "word storms multiples of word clouds for visual comparison of documents"
  Abstract: Word clouds are a popular tool for visualizing documents, but they are not a good tool for comparing documents, because identical words are not presented consistently across different clouds. We introduce the concept of word storms, a visualization tool for analysing corpora of documents. A word storm is a group of word clouds, in which each cloud represents a single document, juxtaposed to allow the viewer to compare and contrast the documents. We present a novel algorithm that creates a coordinated word storm, in which words that appear in multiple documents are placed in the same location, using the same color and orientation, in all of the corresponding clouds. In this way, similar documents are represented by similar-looking word clouds, making them easier to compare and contrast visually. We evaluate the algorithm in two ways: first, an automatic evaluation based on document classification; and second, a user study. The results confirm that unlike standard word clouds, a coordinated word storm better allows for visual comparison of documents.

Connected through hyperedge HE-1 (induced by source paper "rltm an efficient neural ir framework for long documents"):
  with members:
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

Connected through hyperedge HE-2 (induced by source paper "early discovery of emerging entities in microblogs"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "the birth of collective memories analyzing emerging entities in text streams"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "topic representation finding more representative words in topic models"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "integrating document clustering and topic modeling"

Connected through hyperedge HE-4 (induced by source paper "local variation of hashtag spike trains and popularity in twitter"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "attention competition with advertisement"
    - "virality prediction and community structure in social networks"
    - "word statistics in blogs and rss feeds towards empirical universal evidence"

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)