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: "object oriented neural programming oonp for document understanding"
  Abstract: We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.

Connected through hyperedge HE-1 (induced by source paper "open domain question answering using early fusion of knowledge bases and text"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "the web as a knowledge base for answering complex questions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "walk steered convolution for graph classification"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "improved neural relation detection for knowledge base question answering"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-2 (induced by source paper "open vocabulary learning on source code with a graph structured cache"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "a general path based representation for predicting program properties"
    - "generative code modeling with graphs"
    - "code completion with neural attention and pointer networks"
    - "relational inductive biases deep learning and graph networks"
    - "tree to tree neural networks for program translation"
    - "graph memory networks for molecular activity prediction"
    - "neural attribute machines for program generation"

Connected through hyperedge HE-3 (induced by source paper "generalize symbolic knowledge with neural rule engine"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "marrying up regular expressions with neural networks a case study for spoken language understanding"
    - "symbolic priors for rnn based semantic parsing"
    - "advances in pre training distributed word representations"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "harnessing deep neural networks with logic rules"
    - "enriching word vectors with subword information"
    - "learning to compose neural networks for question answering"

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.LG(Machine Learning)