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 "context aware human intent inference for improving human machine cooperation"):
  Abstract: The ability of human beings to precisely recog- nize others intents is a significant mental activity in reasoning about actions, such as, what other people are doing and what they will do next. Recent research has revealed that human intents could be inferred by measuring human cognitive activities through heterogeneous body and brain sensors (e.g., sensors for detecting physiological signals like ECG, brain signals like EEG and IMU sensors like accelerometers and gyros etc.). In this proposal, we aim at developing a computa- tional framework for enabling reliable and precise real-time human intent recognition by measuring human cognitive and physiological activities through the heterogeneous body and brain sensors for improving human machine interactions, and serving intent-based human activity prediction.

Includes members:
  Member "multi person brain activity recognition via comprehensive eeg signal analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning")
  Member "intent recognition in smart living through deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "nips 2016 tutorial generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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.HC(Human-Computer Interaction)