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: "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"
  Abstract: Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer by word-matching the question with a sentence in the context. We demonstrate this issue by constructing adversarial documents that create contradicting answers to the shortcut but do not affect the validity of the original answer. The performance of strong baseline models drops significantly on our adversarial evaluation, indicating that they are indeed exploiting the shortcuts rather than performing multi-hop reasoning. After adversarial training, the baseline's performance improves but is still limited on the adversarial evaluation. Hence, we use a control unit that dynamically attends to the question at different reasoning hops to guide the model's multi-hop reasoning. We show that this 2-hop model trained on the regular data is more robust to the adversaries than the baseline model. After adversarial training, this 2-hop model not only achieves improvements over its counterpart trained on regular data, but also outperforms the adversarially-trained 1-hop baseline. We hope that these insights and initial improvements will motivate the development of new models that combine explicit compositional reasoning with adversarial training.

Connected through hyperedge HE-1 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised question decomposition for question answering"):
  with members:
    - "break it down a question understanding benchmark"
    - "unsupervised question answering by cloze translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "multi hop reading comprehension through question decomposition and rescoring"

Connected through hyperedge HE-3 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-4 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions"):
  with members:
    - "multi paragraph reasoning with knowledge enhanced graph neural network"
    - "select answer and explain interpretable multi hop reading comprehension over multiple documents"
    - "multi hop paragraph retrieval for open domain question answering"
    - "sg net syntax guided machine reading comprehension"
    - "albert a lite bert for self supervised learning of language representations"
    - "dynamically fused graph network for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "hierarchical graph network for multi hop question answering"

Connected through hyperedge HE-5 (induced by source paper "hierarchical graph network for multi hop question answering"):
  with members:
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "understanding dataset design choices for multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-6 (induced by source paper "self assembling modular networks for interpretable multi hop reasoning"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "using syntax to ground referring expressions in natural images"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"

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)