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: "food recommendation using ontology and heuristics"
  Abstract: Recommender systems are needed to find food items of ones interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

Connected through hyperedge HE-1 (induced by source paper "flavour enhanced food recommendation"):
  with members:
    - "a navigational approach to health actionable guidance for improved quality of life"
    - "cross modal health state estimation"

Connected through hyperedge HE-2 (induced by source paper "a survey of food recommenders"):
  with members:
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "recipe recommendation using ingredient networks"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "food recommender systems important contributions challenges and future research directions"):
  with members:
    - "you tweet what you eat studying food consumption through twitter"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-4 (induced by source paper "caco competitive ant colony optimization a nature inspired metaheuristic for large scale global optimization"):
  with members:
    - "collaborating robotics using nature inspired meta heuristics"
    - "obesity heuristic new way on artificial immune systems"

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)