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 "out of domain detection for low resource text classification tasks"):
  Abstract: Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since we observe that training data is often insufficient in machine learning applications. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluation on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.

Includes members:
  Member "imposing label relational inductive bias for extremely fine grained entity typing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained entity typing in hyperbolic space")
  Member "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier")
  Member "one shot relational learning for knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")
  Member "diverse few shot text classification with multiple metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "doc deep open classification of text documents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conditional gaussian distribution learning for open set recognition")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "neural sentence embedding using only in domain sentences for out of domain sentence detection in dialog systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "out of domain detection for natural language understanding in dialog systems")

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)