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: "deep learning evaluation using deep linguistic processing"
  Abstract: We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value on a static and monolithic dataset.

Connected through hyperedge HE-1 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-2 (induced by source paper "how clever is the film model and how clever can it be"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "a dataset and architecture for visual reasoning with a working memory"
    - "compositional attention networks for machine reasoning"
    - "did the model understand the question"
    - "deep reinforcement learning that matters"
    - "inferring and executing programs for visual reasoning"
    - "on the state of the art of evaluation in neural language models"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "a new dataset and model for learning to understand navigational instructions"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "gated attention architectures for task oriented language grounding"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "grounded language learning in a simulated 3d world"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "analyzing the behavior of visual question answering models"
    - "virtual embodiment a scalable long term strategy for artificial intelligence research"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "evaluating syntactic properties of seq2seq output with a broad coverage hpsg a case study on machine translation"):
  with members:
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "a neural conversational model"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)