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: "evaluating theory of mind in question answering"
  Abstract: We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Our tasks are inspired by theory-of-mind experiments that examine whether children are able to reason about the beliefs of others, in particular when those beliefs differ from reality. We evaluate a number of recent neural models with memory augmentation. We find that all fail on our tasks, which require keeping track of inconsistent states of the world; moreover, the models' accuracy decreases notably when random sentences are introduced to the tasks at test.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-2 (induced by source paper "socialiqa commonsense reasoning about social interactions"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "from recognition to cognition visual commonsense reasoning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "annotation artifacts in natural language inference data"
    - "machine common sense concept paper"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-3 (induced by source paper "what do entity centric models learn insights from entity linking in multi party dialogue"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "amore upf at semeval 2018 task 4 bilstm with entity library"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "relnet end to end modeling of entities relations"
    - "improving coreference resolution by learning entity level distributed representations"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "learning global features for coreference resolution"

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)