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 "an analysis of observation length requirements in spoken language for machine understanding of human behaviors"):
  Abstract: Automatic quantification of human interaction behaviors based on language information has been shown to be effective in psychotherapy research domains such as marital therapy and cancer care. Existing systems typically use a moving-window approach where the target behavior construct is first quantified based on observations inside a window, such as a fixed number of words or turns, and then integrated over all the windows in that interaction. Given a behavior of interest, it is important to employ the appropriate length of observation, since too short a window might not contain sufficient information. Unfortunately, the link between behavior and observation length for lexical cues has not been well studied and it is not clear how these requirements relate to the characteristics of the target behavior construct. Therefore, in this paper, we investigate how the choice of window length affects the efficacy of language-based behavior quantification, by analyzing (a) the similarity between system predictions and human expert assessments for the same behavior construct and (b) the consistency in relations between predictions of related behavior constructs. We apply our analysis to a large and diverse set of behavior codes that are used to annotate real-life interactions and find that behaviors related to negative affect can be quantified from just a few words whereas those related to positive traits and problem solving require much longer observation windows. On the other hand, constructs that describe dysphoric affect do not appear to be quantifiable from language information alone, regardless of how long they are observed. We compare our findings with related work on behavior quantification based on acoustic vocal cues as well as with prior work on thin slices and human personality predictions and find that, in general, they are in agreement.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "snapshot ensembles train 1 get m for free":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "on the properties of neural machine translation encoder decoder approaches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "generating sequences with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)