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: "understanding dataset design choices for multi hop reasoning"
  Abstract: Learning multi-hop reasoning has been a key challenge for reading comprehension models, leading to the design of datasets that explicitly focus on it. Ideally, a model should not be able to perform well on a multi-hop question answering task without doing multi-hop reasoning. In this paper, we investigate two recently proposed datasets, WikiHop and HotpotQA. First, we explore sentence-factored models for these tasks; by design, these models cannot do multi-hop reasoning, but they are still able to solve a large number of examples in both datasets. Furthermore, we find spurious correlations in the unmasked version of WikiHop, which make it easy to achieve high performance considering only the questions and answers. Finally, we investigate one key difference between these datasets, namely span-based vs. multiple-choice formulations of the QA task. Multiple-choice versions of both datasets can be easily gamed, and two models we examine only marginally exceed a baseline in this setting. Overall, while these datasets are useful testbeds, high-performing models may not be learning as much multi-hop reasoning as previously thought.

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"
    - "show your work improved reporting of experimental results"

Connected through hyperedge HE-2 (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"
    - "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"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"

Connected through hyperedge HE-3 (induced by source paper "don t take the easy way out ensemble based methods for avoiding known dataset biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "probing the need for visual context in multimodal machine translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "misleading failures of partial input baselines"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-4 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-5 (induced by source paper "what s missing a knowledge gap guided approach for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "careful selection of knowledge to solve open book question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"

Connected through hyperedge HE-6 (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"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "dynamically fused graph network for multi hop reasoning"

Connected through hyperedge HE-7 (induced by source paper "multi hop question answering via reasoning chains"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "compositional questions do not necessitate multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "attention is not explanation"
    - "dynamically fused graph network for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-8 (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"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"
    - "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)