human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards generalizable neuro symbolic systems for commonsense question answering"):
  Abstract: Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries. Recent approaches on such tasks show increased performance, only when models are either pre-trained with additional information or when domain-specific heuristics are used, without any special consideration regarding the knowledge resource type. In this paper, we perform a survey of recent commonsense QA methods and we provide a systematic analysis of popular knowledge resources and knowledge-integration methods, across benchmarks from multiple commonsense datasets. Our results and analysis show that attention-based injection seems to be a preferable choice for knowledge integration and that the degree of domain overlap, between knowledge bases and datasets, plays a crucial role in determining model success.

Includes members:
  Member "hellaswag can a machine really finish your sentence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "improving question answering with external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sirenless reveal the intention behind news")
  Member "augmenting neural networks with first order logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")
  Member "option comparison network for multiple choice reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neuro symbolic architectures for context understanding")
  Member "comet commonsense transformers for automatic knowledge graph construction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "kagnet knowledge aware graph networks for commonsense reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neuro symbolic architectures for context understanding")
  Member "knowledge enhanced contextual word representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "dream a challenge dataset and models for dialogue based reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")

Question: Please tell me which class the source paper 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)