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: "collective evolutionary concept distance based query expansion for effective web document retrieval"
  Abstract: In this work several semantic approaches to concept-based query expansion and re-ranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval. In particular, we focus on concept-based query expansion schemes where, in order to effectively increase the precision of web document retrieval and to decrease the users’ browsing time, the main goal is to quickly provide users with the most suitable query expansion. Two key tasks for query expansion in web document retrieval are to find the expansion candidates, as the closest concepts in web document domain, and to rank the expanded queries properly. The approach we propose aims at improving the expansion phase for better web document retrieval and precision. The basic idea is to measure the distance between candidate concepts using the PMING distance, a collaborative semantic proximity measure, i.e. a measure which can be computed using statistical results from a web search engine. Experiments show that the proposed technique can provide users with more satisfying expansion results and improve the quality of web document retrieval.

Connected through hyperedge HE-1 (induced by source paper "performance effectiveness of multimedia information search using the epsilon greedy algorithm"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "term relevance feedback for contextual named entity retrieval"
    - "darts differentiable architecture search"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "hierarchical representations for efficient architecture search"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "relevance based word embedding"
    - "transitive hashing network for heterogeneous multimedia retrieval"

Connected through hyperedge HE-2 (induced by source paper "discovering latent information by spreading activation algorithm for document retrieval"):
  with members:
    - "semantic search by latent ontological features"

Connected through hyperedge HE-3 (induced by source paper "web based similarity for emotion recognition in web objects"):
  with members:
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

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)