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: "pumice a multi modal agent that learns concepts and conditionals from natural language and demonstrations"
  Abstract: Natural language programming is a promising approach to enable end users to instruct new tasks for intelligent agents. However, our formative study found that end users would often use unclear, ambiguous or vague concepts when naturally instructing tasks in natural language, especially when specifying conditionals. Existing systems have limited support for letting the user teach agents new concepts or explaining unclear concepts. In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations. Users can also define new procedures and concepts by demonstrating and referring to contents within GUIs of existing mobile apps. We demonstrate this approach in PUMICE, an end-user programmable agent that implements this approach. A lab study with 10 users showed its usability.

Connected through hyperedge HE-1 (induced by source paper "convo what does conversational programming need an exploration of machine learning interface design"):
  with members:
    - "a comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech"
    - "program synthesis and semantic parsing with learned code idioms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "meet cyrus the query by voice mobile assistant for the tutoring and formative assessment of sql learners"
    - "abstract syntax networks for code generation and semantic parsing"
    - "attention based models for speech recognition"

Connected through hyperedge HE-2 (induced by source paper "towards effective human ai collaboration in gui based interactive task learning agents"):
  with members:
    - "reinforcement learning on web interfaces using workflow guided exploration"

Connected through hyperedge HE-3 (induced by source paper "vasta a vision and language assisted smartphone task automation system"):
  with members:
    - "statelens a reverse engineering solution for making existing dynamic touchscreens accessible"
    - "yolov3 an incremental improvement"
    - "universal sentence encoder"
    - "universal semantic parsing"
    - "focal loss for dense object detection"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

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.HC(Human-Computer Interaction)