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: "joint and conditional estimation of tagging and parsing models"
  Abstract: This paper compares two different ways of estimating statistical language models. Many statistical NLP tagging and parsing models are estimated by maximizing the (joint) likelihood of the fully-observed training data. However, since these applications only require the conditional probability distributions, these distributions can in principle be learnt by maximizing the conditional likelihood of the training data. Perhaps somewhat surprisingly, models estimated by maximizing the joint were superior to models estimated by maximizing the conditional, even though some of the latter models intuitively had access to ``more information''.

Connected through hyperedge HE-1 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies"):
  with members:
    - "optimizing for measure of performance in max margin parsing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "tighter linear program relaxations for high order graphical models"
    - "discriminative probabilistic models for relational data"
    - "fast exact inference for recursive cardinality models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "tightening lp relaxations for map using message passing"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural network grammars"):
  with members:
    - "easy first dependency parsing with hierarchical tree lstms"
    - "a fast unified model for parsing and sentence understanding"
    - "show attend and tell neural image caption generation with visual attention"
    - "transition based dependency parsing with stack long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a bayesian model for generative transition based dependency parsing"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "a logic based approach to generatively defined discriminative modeling"):
  with members:
    - "comparing bayesian network classifiers"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-4 (induced by source paper "framework and resources for natural language parser evaluation"):
  with members:
    - "a common xml based framework for syntactic annotations"
    - "exploiting diversity in natural language processing combining parsers"
    - "atlas a flexible and extensible architecture for linguistic annotation"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "corpus annotation for parser evaluation"

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)