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: "emergent predication structure in hidden state vectors of neural readers"
  Abstract: A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the emergence of "predication structure" in the hidden state vectors of these readers. More specifically, we provide evidence that the hidden state vectors represent atomic formulas $\Phi[c]$ where $\Phi$ is a semantic property (predicate) and $c$ is a constant symbol entity identifier.

Connected through hyperedge HE-1 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "deep probabilistic logic a unifying framework for indirect supervision"

Connected through hyperedge HE-2 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "why can t memory networks read effectively"):
  with members:
    - "understanding dataset design choices for multi hop reasoning"
    - "misleading failures of partial input baselines"
    - "blindfold baselines for embodied qa"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "clicr a dataset of clinical case reports for machine reading comprehension"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-4 (induced by source paper "entity tracking improves cloze style reading comprehension"):
  with members:
    - "neural models for reasoning over multiple mentions using coreference"
    - "attention over attention neural networks for reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "broad context language modeling as reading comprehension"
    - "text understanding with the attention sum reader network"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "learning global features for coreference resolution"
    - "tracking the world state with recurrent entity networks"

Connected through hyperedge HE-5 (induced by source paper "clicr a dataset of clinical case reports for machine reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "quasar datasets for question answering by search and reading"
    - "question answering from unstructured text by retrieval and comprehension"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "deductive and analogical reasoning on a semantically embedded knowledge graph"):
  with members:
    - "using a distributional semantic vector space with a knowledge base for reasoning in uncertain conditions"
    - "skip thought vectors"
    - "reasoning in vector space an exploratory study of question answering"
    - "distributed representations of words and phrases and their compositionality"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "measuring semantic similarity by latent relational analysis"

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)