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: "cornell spf cornell semantic parsing framework"
  Abstract: The Cornell Semantic Parsing Framework (SPF) is a learning and inference framework for mapping natural language to formal representation of its meaning.

Connected through hyperedge HE-1 (induced by source paper "text2math end to end parsing text into math expressions"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "semantic parsing with syntax and table aware sql generation"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "a meaning based statistical english math word problem solver"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"

Connected through hyperedge HE-2 (induced by source paper "augmenting knowledge through statistical goal oriented human robot dialog"):
  with members:
    - "goal oriented dialogue policy learning from failures"
    - "the strands project long term autonomy in everyday environments"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "optimizing dialogue management with reinforcement learning experiments with the njfun system"

Connected through hyperedge HE-3 (induced by source paper "macro grammars and holistic triggering for efficient semantic parsing"):
  with members:
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "data recombination for neural semantic parsing"
    - "semantic parsing with semi supervised sequential autoencoders"
    - "neural programmer inducing latent programs with gradient descent"
    - "compositional semantic parsing on semi structured tables"

Connected through hyperedge HE-4 (induced by source paper "data recombination for neural semantic parsing"):
  with members:
    - "pointing the unknown words"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "effective approaches to attention based neural machine translation"
    - "transition based dependency parsing with stack long short term memory"
    - "traversing knowledge graphs in vector space"
    - "a deep architecture for semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "inferring logical forms from denotations"):
  with members:
    - "neural programmer inducing latent programs with gradient descent"
    - "neural enquirer learning to query tables with natural language"
    - "compositional semantic parsing on semi structured tables"
    - "neural programmer interpreters"
    - "traversing knowledge graphs in vector space"
    - "uw spf the university of washington semantic parsing framework"
    - "lambda dependency based compositional semantics"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

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)