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: "stacking classifiers for anti spam filtering of e mail"
  Abstract: We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.

Connected through hyperedge HE-1 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks"):
  with members:
    - "using machine learning and big data analytics to prioritize outpatients in hetnets"
    - "service embedding in iot networks"
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-2 (induced by source paper "funnelling a new ensemble method for heterogeneous transfer learning and its application to cross lingual text classification"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "distributed representations of words and phrases and their compositionality"
    - "generating sequences with recurrent neural networks"
    - "exploiting similarities among languages for machine translation"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"
    - "issues in stacked generalization"

Connected through hyperedge HE-3 (induced by source paper "deep ensemble learning for alzheimers disease classification"):
  with members:
    - "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging"

Connected through hyperedge HE-4 (induced by source paper "feature weighted linear stacking"):
  with members:
    - "issues in stacked generalization"

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.CL(Computation and Language)