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: "scarcity of crossing dependencies a direct outcome of a specific constraint"
  Abstract: The structure of a sentence can be represented as a network where vertices are words and edges indicate syntactic dependencies. Interestingly, crossing syntactic dependencies have been observed to be infrequent in human languages. This leads to the question of whether the scarcity of crossings in languages arises from an independent and specific constraint on crossings. We provide statistical evidence suggesting that this is not the case, as the proportion of dependency crossings of sentences from a wide range of languages can be accurately estimated by a simple predictor based on a null hypothesis on the local probability that two dependencies cross given their lengths. The relative error of this predictor never exceeds #R##N#5#R##N#%#R##N# on average, whereas the error of a baseline predictor assuming a random ordering of the words of a sentence is at least six times greater. Our results suggest that the low frequency of crossings in natural languages is neither originated by hidden knowledge of language nor by the undesirability of crossings per se, but as a mere side effect of the principle of dependency length minimization.

Connected through hyperedge HE-1 (induced by source paper "anti dependency distance minimization in short sequences a graph theoretic approach"):
  with members:
    - "the placement of the head that maximizes predictability an information theoretic approach"
    - "towards a theory of word order comment on dependency distance a new perspective on syntactic patterns in natural language by haitao liu et al"
    - "non crossing dependencies least effort not grammar"
    - "liberating language research from dogmas of the 20th century"
    - "a commentary on the now or never bottleneck a fundamental constraint on language by christiansen and chater 2016"
    - "the risks of mixing dependency lengths from sequences of different length"
    - "random crossings in dependency trees"
    - "hubiness length crossings and their relationships in dependency trees"

Connected through hyperedge HE-2 (induced by source paper "edge crossings in random linear arrangements"):
  with members:
    - "on the maximum crossing number"
    - "crossing patterns in nonplanar road networks"
    - "non crossing dependencies least effort not grammar"
    - "crossings as a side effect of dependency lengths"
    - "liberating language research from dogmas of the 20th century"
    - "a stronger null hypothesis for crossing dependencies"
    - "random crossings in dependency trees"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-3 (induced by source paper "on the relation between dependency distance crossing dependencies and parsing comment on dependency distance a new perspective on syntactic patterns in natural languages by haitao liu et al"):
  with members:
    - "syntaxnet models for the conll 2017 shared task"
    - "globally normalized transition based neural networks"
    - "non crossing dependencies least effort not grammar"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "crossings as a side effect of dependency lengths"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-4 (induced by source paper "liberating language research from dogmas of the 20th century"):
  with members:
    - "crossings as a side effect of dependency lengths"
    - "dependency length minimization puzzles and promises"
    - "reply to the commentary be careful when assuming the obvious by p alday"
    - "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)