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: "the optimality of attaching unlinked labels to unlinked meanings"
  Abstract: Vocabulary learning by children can be characterized by many biases. When encountering a new word, children as well as adults, are biased towards assuming that it means something totally different from the words that they already know. To the best of our knowledge, the 1st mathematical proof of the optimality of this bias is presented here. First, it is shown that this bias is a particular case of the maximization of mutual information between words and meanings. Second, the optimality is proven within a more general information theoretic framework where mutual information maximization competes with other information theoretic principles. The bias is a prediction from modern information theory. The relationship between information theoretic principles and the principles of contrast and mutual exclusivity is also shown.

Connected through hyperedge HE-1 (induced by source paper "the origins of zipf s meaning frequency law"):
  with members:
    - "compression and the origins of zipf s law for word frequencies"
    - "the meaning frequency law in zipfian optimization models of communication"

Connected through hyperedge HE-2 (induced by source paper "a commentary on the now or never bottleneck a fundamental constraint on language by christiansen and chater 2015"):
  with members:
    - "compression and the origins of zipf s law for word frequencies"
    - "crossings as a side effect of dependency lengths"
    - "compression and the origins of zipf s law of abbreviation"
    - "reply to the commentary be careful when assuming the obvious by p alday"
    - "the risks of mixing dependency lengths from sequences of different length"
    - "a stronger null hypothesis for crossing dependencies"
    - "the placement of the head that minimizes online memory a complex systems approach"
    - "why sov might be initially preferred and then lost or recovered a theoretical framework"

Connected through hyperedge HE-3 (induced by source paper "compression and the origins of zipf s law of abbreviation"):
  with members:
    - "rank diversity of languages generic behavior in computational linguistics"
    - "be careful when assuming the obvious commentary on the placement of the head that minimizes online memory a complex systems approach"
    - "a stronger null hypothesis for crossing dependencies"
    - "the placement of the head that minimizes online memory a complex systems approach"

Connected through hyperedge HE-4 (induced by source paper "the meaning frequency law in zipfian optimization models of communication"):
  with members:
    - "be careful when assuming the obvious commentary on the placement of the head that minimizes online memory a complex systems approach"

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)